2000 character limit reached
A Conditioned Unsupervised Regression Framework Attuned to the Dynamic Nature of Data Streams
Published 12 Dec 2023 in cs.LG | (2312.07682v2)
Abstract: In scenarios where obtaining real-time labels proves challenging, conventional approaches may result in sub-optimal performance. This paper presents an optimal strategy for streaming contexts with limited labeled data, introducing an adaptive technique for unsupervised regression. The proposed method leverages a sparse set of initial labels and introduces an innovative drift detection mechanism to enable dynamic model adaptations in response to evolving patterns in the data. To enhance adaptability, we integrate the ADWIN (ADaptive WINdowing) algorithm with error generalization based on Root Mean Square Error (RMSE). ADWIN facilitates real-time drift detection, while RMSE provides a robust measure of model prediction accuracy. This combination enables our multivariate method to effectively navigate the challenges of streaming data, continuously adapting to changing patterns while maintaining a high level of predictive precision. We evaluate the performance of our multivariate method across various public datasets, comparing it to non-adapting baselines. Through comprehensive assessments, we demonstrate the superior efficacy of our adaptive regression technique for tasks where obtaining labels in real-time is a significant challenge. The results underscore the method's capacity to outperform traditional approaches and highlight its potential in scenarios characterized by label scarcity and evolving data patterns.
- Céspedes-Sisniega, J., López-García, Á.: Frouros: A python library for drift detection in machine learning systems. arXiv preprint arXiv:2208.06868 (2022) Baena-Garcıa et al. [2006] Baena-Garcıa, M., Campo-Ávila, J., Fidalgo, R., Bifet, A.: Early drift detection method. In: Fourth International Workshop on Knowledge Discovery from Data Streams, vol. 6 (2006). Citeseer Moreno-Torres et al. [2012] Moreno-Torres, J.G., Raeder, T., Alaiz-Rodríguez, R., Chawla, N.V., Herrera, F.: A unifying view on dataset shift in classification. Pattern recognition 45(1), 521–530 (2012) Gemaque et al. [2020] Gemaque, R.N., Costa, A.F.J., Giusti, R., Dos Santos, E.M.: An overview of unsupervised drift detection methods. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 10(6), 1381 (2020) Gama et al. [2004] Gama, J., Medas, P., Castillo, G., Rodrigues, P.: Learning with drift detection. In: Advances in Artificial Intelligence–SBIA 2004: 17th Brazilian Symposium on Artificial Intelligence, Sao Luis, Maranhao, Brazil, September 29-Ocotber 1, 2004. Proceedings 17, pp. 286–295 (2004). Springer Bifet and Gavalda [2007] Bifet, A., Gavalda, R.: Learning from time-changing data with adaptive windowing. In: Proceedings of the 2007 SIAM International Conference on Data Mining, pp. 443–448 (2007). SIAM Lu et al. [2018] Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., Zhang, G.: Learning under concept drift: A review. IEEE transactions on knowledge and data engineering 31(12), 2346–2363 (2018) Novac et al. [2020] Novac, P.-E., Castagnetti, A., Russo, A., Miramond, B., Pegatoquet, A., Verdier, F.: Toward unsupervised human activity recognition on microcontroller units. In: 2020 23rd Euromicro Conference on Digital System Design (DSD), pp. 542–550 (2020). IEEE Ravaglia et al. [2021] Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Baena-Garcıa, M., Campo-Ávila, J., Fidalgo, R., Bifet, A.: Early drift detection method. In: Fourth International Workshop on Knowledge Discovery from Data Streams, vol. 6 (2006). Citeseer Moreno-Torres et al. [2012] Moreno-Torres, J.G., Raeder, T., Alaiz-Rodríguez, R., Chawla, N.V., Herrera, F.: A unifying view on dataset shift in classification. Pattern recognition 45(1), 521–530 (2012) Gemaque et al. [2020] Gemaque, R.N., Costa, A.F.J., Giusti, R., Dos Santos, E.M.: An overview of unsupervised drift detection methods. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 10(6), 1381 (2020) Gama et al. [2004] Gama, J., Medas, P., Castillo, G., Rodrigues, P.: Learning with drift detection. In: Advances in Artificial Intelligence–SBIA 2004: 17th Brazilian Symposium on Artificial Intelligence, Sao Luis, Maranhao, Brazil, September 29-Ocotber 1, 2004. Proceedings 17, pp. 286–295 (2004). Springer Bifet and Gavalda [2007] Bifet, A., Gavalda, R.: Learning from time-changing data with adaptive windowing. In: Proceedings of the 2007 SIAM International Conference on Data Mining, pp. 443–448 (2007). SIAM Lu et al. [2018] Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., Zhang, G.: Learning under concept drift: A review. IEEE transactions on knowledge and data engineering 31(12), 2346–2363 (2018) Novac et al. [2020] Novac, P.-E., Castagnetti, A., Russo, A., Miramond, B., Pegatoquet, A., Verdier, F.: Toward unsupervised human activity recognition on microcontroller units. In: 2020 23rd Euromicro Conference on Digital System Design (DSD), pp. 542–550 (2020). IEEE Ravaglia et al. [2021] Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Moreno-Torres, J.G., Raeder, T., Alaiz-Rodríguez, R., Chawla, N.V., Herrera, F.: A unifying view on dataset shift in classification. Pattern recognition 45(1), 521–530 (2012) Gemaque et al. [2020] Gemaque, R.N., Costa, A.F.J., Giusti, R., Dos Santos, E.M.: An overview of unsupervised drift detection methods. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 10(6), 1381 (2020) Gama et al. [2004] Gama, J., Medas, P., Castillo, G., Rodrigues, P.: Learning with drift detection. In: Advances in Artificial Intelligence–SBIA 2004: 17th Brazilian Symposium on Artificial Intelligence, Sao Luis, Maranhao, Brazil, September 29-Ocotber 1, 2004. Proceedings 17, pp. 286–295 (2004). Springer Bifet and Gavalda [2007] Bifet, A., Gavalda, R.: Learning from time-changing data with adaptive windowing. In: Proceedings of the 2007 SIAM International Conference on Data Mining, pp. 443–448 (2007). SIAM Lu et al. [2018] Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., Zhang, G.: Learning under concept drift: A review. IEEE transactions on knowledge and data engineering 31(12), 2346–2363 (2018) Novac et al. [2020] Novac, P.-E., Castagnetti, A., Russo, A., Miramond, B., Pegatoquet, A., Verdier, F.: Toward unsupervised human activity recognition on microcontroller units. In: 2020 23rd Euromicro Conference on Digital System Design (DSD), pp. 542–550 (2020). IEEE Ravaglia et al. [2021] Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Gemaque, R.N., Costa, A.F.J., Giusti, R., Dos Santos, E.M.: An overview of unsupervised drift detection methods. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 10(6), 1381 (2020) Gama et al. [2004] Gama, J., Medas, P., Castillo, G., Rodrigues, P.: Learning with drift detection. In: Advances in Artificial Intelligence–SBIA 2004: 17th Brazilian Symposium on Artificial Intelligence, Sao Luis, Maranhao, Brazil, September 29-Ocotber 1, 2004. Proceedings 17, pp. 286–295 (2004). Springer Bifet and Gavalda [2007] Bifet, A., Gavalda, R.: Learning from time-changing data with adaptive windowing. In: Proceedings of the 2007 SIAM International Conference on Data Mining, pp. 443–448 (2007). SIAM Lu et al. [2018] Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., Zhang, G.: Learning under concept drift: A review. IEEE transactions on knowledge and data engineering 31(12), 2346–2363 (2018) Novac et al. [2020] Novac, P.-E., Castagnetti, A., Russo, A., Miramond, B., Pegatoquet, A., Verdier, F.: Toward unsupervised human activity recognition on microcontroller units. In: 2020 23rd Euromicro Conference on Digital System Design (DSD), pp. 542–550 (2020). IEEE Ravaglia et al. [2021] Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Gama, J., Medas, P., Castillo, G., Rodrigues, P.: Learning with drift detection. In: Advances in Artificial Intelligence–SBIA 2004: 17th Brazilian Symposium on Artificial Intelligence, Sao Luis, Maranhao, Brazil, September 29-Ocotber 1, 2004. Proceedings 17, pp. 286–295 (2004). Springer Bifet and Gavalda [2007] Bifet, A., Gavalda, R.: Learning from time-changing data with adaptive windowing. In: Proceedings of the 2007 SIAM International Conference on Data Mining, pp. 443–448 (2007). SIAM Lu et al. [2018] Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., Zhang, G.: Learning under concept drift: A review. IEEE transactions on knowledge and data engineering 31(12), 2346–2363 (2018) Novac et al. [2020] Novac, P.-E., Castagnetti, A., Russo, A., Miramond, B., Pegatoquet, A., Verdier, F.: Toward unsupervised human activity recognition on microcontroller units. In: 2020 23rd Euromicro Conference on Digital System Design (DSD), pp. 542–550 (2020). IEEE Ravaglia et al. [2021] Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Bifet, A., Gavalda, R.: Learning from time-changing data with adaptive windowing. In: Proceedings of the 2007 SIAM International Conference on Data Mining, pp. 443–448 (2007). SIAM Lu et al. [2018] Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., Zhang, G.: Learning under concept drift: A review. IEEE transactions on knowledge and data engineering 31(12), 2346–2363 (2018) Novac et al. [2020] Novac, P.-E., Castagnetti, A., Russo, A., Miramond, B., Pegatoquet, A., Verdier, F.: Toward unsupervised human activity recognition on microcontroller units. In: 2020 23rd Euromicro Conference on Digital System Design (DSD), pp. 542–550 (2020). IEEE Ravaglia et al. [2021] Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., Zhang, G.: Learning under concept drift: A review. IEEE transactions on knowledge and data engineering 31(12), 2346–2363 (2018) Novac et al. [2020] Novac, P.-E., Castagnetti, A., Russo, A., Miramond, B., Pegatoquet, A., Verdier, F.: Toward unsupervised human activity recognition on microcontroller units. In: 2020 23rd Euromicro Conference on Digital System Design (DSD), pp. 542–550 (2020). IEEE Ravaglia et al. [2021] Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Novac, P.-E., Castagnetti, A., Russo, A., Miramond, B., Pegatoquet, A., Verdier, F.: Toward unsupervised human activity recognition on microcontroller units. In: 2020 23rd Euromicro Conference on Digital System Design (DSD), pp. 542–550 (2020). IEEE Ravaglia et al. [2021] Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021)
- Baena-Garcıa, M., Campo-Ávila, J., Fidalgo, R., Bifet, A.: Early drift detection method. In: Fourth International Workshop on Knowledge Discovery from Data Streams, vol. 6 (2006). Citeseer Moreno-Torres et al. [2012] Moreno-Torres, J.G., Raeder, T., Alaiz-Rodríguez, R., Chawla, N.V., Herrera, F.: A unifying view on dataset shift in classification. Pattern recognition 45(1), 521–530 (2012) Gemaque et al. [2020] Gemaque, R.N., Costa, A.F.J., Giusti, R., Dos Santos, E.M.: An overview of unsupervised drift detection methods. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 10(6), 1381 (2020) Gama et al. [2004] Gama, J., Medas, P., Castillo, G., Rodrigues, P.: Learning with drift detection. In: Advances in Artificial Intelligence–SBIA 2004: 17th Brazilian Symposium on Artificial Intelligence, Sao Luis, Maranhao, Brazil, September 29-Ocotber 1, 2004. Proceedings 17, pp. 286–295 (2004). Springer Bifet and Gavalda [2007] Bifet, A., Gavalda, R.: Learning from time-changing data with adaptive windowing. In: Proceedings of the 2007 SIAM International Conference on Data Mining, pp. 443–448 (2007). SIAM Lu et al. [2018] Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., Zhang, G.: Learning under concept drift: A review. IEEE transactions on knowledge and data engineering 31(12), 2346–2363 (2018) Novac et al. [2020] Novac, P.-E., Castagnetti, A., Russo, A., Miramond, B., Pegatoquet, A., Verdier, F.: Toward unsupervised human activity recognition on microcontroller units. In: 2020 23rd Euromicro Conference on Digital System Design (DSD), pp. 542–550 (2020). IEEE Ravaglia et al. [2021] Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Moreno-Torres, J.G., Raeder, T., Alaiz-Rodríguez, R., Chawla, N.V., Herrera, F.: A unifying view on dataset shift in classification. Pattern recognition 45(1), 521–530 (2012) Gemaque et al. [2020] Gemaque, R.N., Costa, A.F.J., Giusti, R., Dos Santos, E.M.: An overview of unsupervised drift detection methods. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 10(6), 1381 (2020) Gama et al. [2004] Gama, J., Medas, P., Castillo, G., Rodrigues, P.: Learning with drift detection. In: Advances in Artificial Intelligence–SBIA 2004: 17th Brazilian Symposium on Artificial Intelligence, Sao Luis, Maranhao, Brazil, September 29-Ocotber 1, 2004. Proceedings 17, pp. 286–295 (2004). Springer Bifet and Gavalda [2007] Bifet, A., Gavalda, R.: Learning from time-changing data with adaptive windowing. In: Proceedings of the 2007 SIAM International Conference on Data Mining, pp. 443–448 (2007). SIAM Lu et al. [2018] Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., Zhang, G.: Learning under concept drift: A review. IEEE transactions on knowledge and data engineering 31(12), 2346–2363 (2018) Novac et al. [2020] Novac, P.-E., Castagnetti, A., Russo, A., Miramond, B., Pegatoquet, A., Verdier, F.: Toward unsupervised human activity recognition on microcontroller units. In: 2020 23rd Euromicro Conference on Digital System Design (DSD), pp. 542–550 (2020). IEEE Ravaglia et al. [2021] Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Gemaque, R.N., Costa, A.F.J., Giusti, R., Dos Santos, E.M.: An overview of unsupervised drift detection methods. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 10(6), 1381 (2020) Gama et al. [2004] Gama, J., Medas, P., Castillo, G., Rodrigues, P.: Learning with drift detection. In: Advances in Artificial Intelligence–SBIA 2004: 17th Brazilian Symposium on Artificial Intelligence, Sao Luis, Maranhao, Brazil, September 29-Ocotber 1, 2004. Proceedings 17, pp. 286–295 (2004). Springer Bifet and Gavalda [2007] Bifet, A., Gavalda, R.: Learning from time-changing data with adaptive windowing. In: Proceedings of the 2007 SIAM International Conference on Data Mining, pp. 443–448 (2007). SIAM Lu et al. [2018] Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., Zhang, G.: Learning under concept drift: A review. IEEE transactions on knowledge and data engineering 31(12), 2346–2363 (2018) Novac et al. [2020] Novac, P.-E., Castagnetti, A., Russo, A., Miramond, B., Pegatoquet, A., Verdier, F.: Toward unsupervised human activity recognition on microcontroller units. In: 2020 23rd Euromicro Conference on Digital System Design (DSD), pp. 542–550 (2020). IEEE Ravaglia et al. [2021] Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Gama, J., Medas, P., Castillo, G., Rodrigues, P.: Learning with drift detection. In: Advances in Artificial Intelligence–SBIA 2004: 17th Brazilian Symposium on Artificial Intelligence, Sao Luis, Maranhao, Brazil, September 29-Ocotber 1, 2004. Proceedings 17, pp. 286–295 (2004). Springer Bifet and Gavalda [2007] Bifet, A., Gavalda, R.: Learning from time-changing data with adaptive windowing. In: Proceedings of the 2007 SIAM International Conference on Data Mining, pp. 443–448 (2007). SIAM Lu et al. [2018] Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., Zhang, G.: Learning under concept drift: A review. IEEE transactions on knowledge and data engineering 31(12), 2346–2363 (2018) Novac et al. [2020] Novac, P.-E., Castagnetti, A., Russo, A., Miramond, B., Pegatoquet, A., Verdier, F.: Toward unsupervised human activity recognition on microcontroller units. In: 2020 23rd Euromicro Conference on Digital System Design (DSD), pp. 542–550 (2020). IEEE Ravaglia et al. [2021] Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Bifet, A., Gavalda, R.: Learning from time-changing data with adaptive windowing. In: Proceedings of the 2007 SIAM International Conference on Data Mining, pp. 443–448 (2007). SIAM Lu et al. [2018] Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., Zhang, G.: Learning under concept drift: A review. IEEE transactions on knowledge and data engineering 31(12), 2346–2363 (2018) Novac et al. [2020] Novac, P.-E., Castagnetti, A., Russo, A., Miramond, B., Pegatoquet, A., Verdier, F.: Toward unsupervised human activity recognition on microcontroller units. In: 2020 23rd Euromicro Conference on Digital System Design (DSD), pp. 542–550 (2020). IEEE Ravaglia et al. [2021] Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., Zhang, G.: Learning under concept drift: A review. IEEE transactions on knowledge and data engineering 31(12), 2346–2363 (2018) Novac et al. [2020] Novac, P.-E., Castagnetti, A., Russo, A., Miramond, B., Pegatoquet, A., Verdier, F.: Toward unsupervised human activity recognition on microcontroller units. In: 2020 23rd Euromicro Conference on Digital System Design (DSD), pp. 542–550 (2020). IEEE Ravaglia et al. [2021] Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Novac, P.-E., Castagnetti, A., Russo, A., Miramond, B., Pegatoquet, A., Verdier, F.: Toward unsupervised human activity recognition on microcontroller units. In: 2020 23rd Euromicro Conference on Digital System Design (DSD), pp. 542–550 (2020). IEEE Ravaglia et al. [2021] Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021)
- Moreno-Torres, J.G., Raeder, T., Alaiz-Rodríguez, R., Chawla, N.V., Herrera, F.: A unifying view on dataset shift in classification. Pattern recognition 45(1), 521–530 (2012) Gemaque et al. [2020] Gemaque, R.N., Costa, A.F.J., Giusti, R., Dos Santos, E.M.: An overview of unsupervised drift detection methods. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 10(6), 1381 (2020) Gama et al. [2004] Gama, J., Medas, P., Castillo, G., Rodrigues, P.: Learning with drift detection. In: Advances in Artificial Intelligence–SBIA 2004: 17th Brazilian Symposium on Artificial Intelligence, Sao Luis, Maranhao, Brazil, September 29-Ocotber 1, 2004. Proceedings 17, pp. 286–295 (2004). Springer Bifet and Gavalda [2007] Bifet, A., Gavalda, R.: Learning from time-changing data with adaptive windowing. In: Proceedings of the 2007 SIAM International Conference on Data Mining, pp. 443–448 (2007). SIAM Lu et al. [2018] Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., Zhang, G.: Learning under concept drift: A review. IEEE transactions on knowledge and data engineering 31(12), 2346–2363 (2018) Novac et al. [2020] Novac, P.-E., Castagnetti, A., Russo, A., Miramond, B., Pegatoquet, A., Verdier, F.: Toward unsupervised human activity recognition on microcontroller units. In: 2020 23rd Euromicro Conference on Digital System Design (DSD), pp. 542–550 (2020). IEEE Ravaglia et al. [2021] Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Gemaque, R.N., Costa, A.F.J., Giusti, R., Dos Santos, E.M.: An overview of unsupervised drift detection methods. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 10(6), 1381 (2020) Gama et al. [2004] Gama, J., Medas, P., Castillo, G., Rodrigues, P.: Learning with drift detection. In: Advances in Artificial Intelligence–SBIA 2004: 17th Brazilian Symposium on Artificial Intelligence, Sao Luis, Maranhao, Brazil, September 29-Ocotber 1, 2004. Proceedings 17, pp. 286–295 (2004). Springer Bifet and Gavalda [2007] Bifet, A., Gavalda, R.: Learning from time-changing data with adaptive windowing. In: Proceedings of the 2007 SIAM International Conference on Data Mining, pp. 443–448 (2007). SIAM Lu et al. [2018] Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., Zhang, G.: Learning under concept drift: A review. IEEE transactions on knowledge and data engineering 31(12), 2346–2363 (2018) Novac et al. [2020] Novac, P.-E., Castagnetti, A., Russo, A., Miramond, B., Pegatoquet, A., Verdier, F.: Toward unsupervised human activity recognition on microcontroller units. In: 2020 23rd Euromicro Conference on Digital System Design (DSD), pp. 542–550 (2020). IEEE Ravaglia et al. [2021] Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Gama, J., Medas, P., Castillo, G., Rodrigues, P.: Learning with drift detection. In: Advances in Artificial Intelligence–SBIA 2004: 17th Brazilian Symposium on Artificial Intelligence, Sao Luis, Maranhao, Brazil, September 29-Ocotber 1, 2004. Proceedings 17, pp. 286–295 (2004). Springer Bifet and Gavalda [2007] Bifet, A., Gavalda, R.: Learning from time-changing data with adaptive windowing. In: Proceedings of the 2007 SIAM International Conference on Data Mining, pp. 443–448 (2007). SIAM Lu et al. [2018] Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., Zhang, G.: Learning under concept drift: A review. IEEE transactions on knowledge and data engineering 31(12), 2346–2363 (2018) Novac et al. [2020] Novac, P.-E., Castagnetti, A., Russo, A., Miramond, B., Pegatoquet, A., Verdier, F.: Toward unsupervised human activity recognition on microcontroller units. In: 2020 23rd Euromicro Conference on Digital System Design (DSD), pp. 542–550 (2020). IEEE Ravaglia et al. [2021] Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Bifet, A., Gavalda, R.: Learning from time-changing data with adaptive windowing. In: Proceedings of the 2007 SIAM International Conference on Data Mining, pp. 443–448 (2007). SIAM Lu et al. [2018] Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., Zhang, G.: Learning under concept drift: A review. IEEE transactions on knowledge and data engineering 31(12), 2346–2363 (2018) Novac et al. [2020] Novac, P.-E., Castagnetti, A., Russo, A., Miramond, B., Pegatoquet, A., Verdier, F.: Toward unsupervised human activity recognition on microcontroller units. In: 2020 23rd Euromicro Conference on Digital System Design (DSD), pp. 542–550 (2020). IEEE Ravaglia et al. [2021] Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., Zhang, G.: Learning under concept drift: A review. IEEE transactions on knowledge and data engineering 31(12), 2346–2363 (2018) Novac et al. [2020] Novac, P.-E., Castagnetti, A., Russo, A., Miramond, B., Pegatoquet, A., Verdier, F.: Toward unsupervised human activity recognition on microcontroller units. In: 2020 23rd Euromicro Conference on Digital System Design (DSD), pp. 542–550 (2020). IEEE Ravaglia et al. [2021] Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Novac, P.-E., Castagnetti, A., Russo, A., Miramond, B., Pegatoquet, A., Verdier, F.: Toward unsupervised human activity recognition on microcontroller units. In: 2020 23rd Euromicro Conference on Digital System Design (DSD), pp. 542–550 (2020). IEEE Ravaglia et al. [2021] Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021)
- Gemaque, R.N., Costa, A.F.J., Giusti, R., Dos Santos, E.M.: An overview of unsupervised drift detection methods. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 10(6), 1381 (2020) Gama et al. [2004] Gama, J., Medas, P., Castillo, G., Rodrigues, P.: Learning with drift detection. In: Advances in Artificial Intelligence–SBIA 2004: 17th Brazilian Symposium on Artificial Intelligence, Sao Luis, Maranhao, Brazil, September 29-Ocotber 1, 2004. Proceedings 17, pp. 286–295 (2004). Springer Bifet and Gavalda [2007] Bifet, A., Gavalda, R.: Learning from time-changing data with adaptive windowing. In: Proceedings of the 2007 SIAM International Conference on Data Mining, pp. 443–448 (2007). SIAM Lu et al. [2018] Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., Zhang, G.: Learning under concept drift: A review. IEEE transactions on knowledge and data engineering 31(12), 2346–2363 (2018) Novac et al. [2020] Novac, P.-E., Castagnetti, A., Russo, A., Miramond, B., Pegatoquet, A., Verdier, F.: Toward unsupervised human activity recognition on microcontroller units. In: 2020 23rd Euromicro Conference on Digital System Design (DSD), pp. 542–550 (2020). IEEE Ravaglia et al. [2021] Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Gama, J., Medas, P., Castillo, G., Rodrigues, P.: Learning with drift detection. In: Advances in Artificial Intelligence–SBIA 2004: 17th Brazilian Symposium on Artificial Intelligence, Sao Luis, Maranhao, Brazil, September 29-Ocotber 1, 2004. Proceedings 17, pp. 286–295 (2004). Springer Bifet and Gavalda [2007] Bifet, A., Gavalda, R.: Learning from time-changing data with adaptive windowing. In: Proceedings of the 2007 SIAM International Conference on Data Mining, pp. 443–448 (2007). SIAM Lu et al. [2018] Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., Zhang, G.: Learning under concept drift: A review. IEEE transactions on knowledge and data engineering 31(12), 2346–2363 (2018) Novac et al. [2020] Novac, P.-E., Castagnetti, A., Russo, A., Miramond, B., Pegatoquet, A., Verdier, F.: Toward unsupervised human activity recognition on microcontroller units. In: 2020 23rd Euromicro Conference on Digital System Design (DSD), pp. 542–550 (2020). IEEE Ravaglia et al. [2021] Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Bifet, A., Gavalda, R.: Learning from time-changing data with adaptive windowing. In: Proceedings of the 2007 SIAM International Conference on Data Mining, pp. 443–448 (2007). SIAM Lu et al. [2018] Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., Zhang, G.: Learning under concept drift: A review. IEEE transactions on knowledge and data engineering 31(12), 2346–2363 (2018) Novac et al. [2020] Novac, P.-E., Castagnetti, A., Russo, A., Miramond, B., Pegatoquet, A., Verdier, F.: Toward unsupervised human activity recognition on microcontroller units. In: 2020 23rd Euromicro Conference on Digital System Design (DSD), pp. 542–550 (2020). IEEE Ravaglia et al. [2021] Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., Zhang, G.: Learning under concept drift: A review. IEEE transactions on knowledge and data engineering 31(12), 2346–2363 (2018) Novac et al. [2020] Novac, P.-E., Castagnetti, A., Russo, A., Miramond, B., Pegatoquet, A., Verdier, F.: Toward unsupervised human activity recognition on microcontroller units. In: 2020 23rd Euromicro Conference on Digital System Design (DSD), pp. 542–550 (2020). IEEE Ravaglia et al. [2021] Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Novac, P.-E., Castagnetti, A., Russo, A., Miramond, B., Pegatoquet, A., Verdier, F.: Toward unsupervised human activity recognition on microcontroller units. In: 2020 23rd Euromicro Conference on Digital System Design (DSD), pp. 542–550 (2020). IEEE Ravaglia et al. [2021] Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021)
- Gama, J., Medas, P., Castillo, G., Rodrigues, P.: Learning with drift detection. In: Advances in Artificial Intelligence–SBIA 2004: 17th Brazilian Symposium on Artificial Intelligence, Sao Luis, Maranhao, Brazil, September 29-Ocotber 1, 2004. Proceedings 17, pp. 286–295 (2004). Springer Bifet and Gavalda [2007] Bifet, A., Gavalda, R.: Learning from time-changing data with adaptive windowing. In: Proceedings of the 2007 SIAM International Conference on Data Mining, pp. 443–448 (2007). SIAM Lu et al. [2018] Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., Zhang, G.: Learning under concept drift: A review. IEEE transactions on knowledge and data engineering 31(12), 2346–2363 (2018) Novac et al. [2020] Novac, P.-E., Castagnetti, A., Russo, A., Miramond, B., Pegatoquet, A., Verdier, F.: Toward unsupervised human activity recognition on microcontroller units. In: 2020 23rd Euromicro Conference on Digital System Design (DSD), pp. 542–550 (2020). IEEE Ravaglia et al. [2021] Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Bifet, A., Gavalda, R.: Learning from time-changing data with adaptive windowing. In: Proceedings of the 2007 SIAM International Conference on Data Mining, pp. 443–448 (2007). SIAM Lu et al. [2018] Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., Zhang, G.: Learning under concept drift: A review. IEEE transactions on knowledge and data engineering 31(12), 2346–2363 (2018) Novac et al. [2020] Novac, P.-E., Castagnetti, A., Russo, A., Miramond, B., Pegatoquet, A., Verdier, F.: Toward unsupervised human activity recognition on microcontroller units. In: 2020 23rd Euromicro Conference on Digital System Design (DSD), pp. 542–550 (2020). IEEE Ravaglia et al. [2021] Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., Zhang, G.: Learning under concept drift: A review. IEEE transactions on knowledge and data engineering 31(12), 2346–2363 (2018) Novac et al. [2020] Novac, P.-E., Castagnetti, A., Russo, A., Miramond, B., Pegatoquet, A., Verdier, F.: Toward unsupervised human activity recognition on microcontroller units. In: 2020 23rd Euromicro Conference on Digital System Design (DSD), pp. 542–550 (2020). IEEE Ravaglia et al. [2021] Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Novac, P.-E., Castagnetti, A., Russo, A., Miramond, B., Pegatoquet, A., Verdier, F.: Toward unsupervised human activity recognition on microcontroller units. In: 2020 23rd Euromicro Conference on Digital System Design (DSD), pp. 542–550 (2020). IEEE Ravaglia et al. [2021] Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021)
- Bifet, A., Gavalda, R.: Learning from time-changing data with adaptive windowing. In: Proceedings of the 2007 SIAM International Conference on Data Mining, pp. 443–448 (2007). SIAM Lu et al. [2018] Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., Zhang, G.: Learning under concept drift: A review. IEEE transactions on knowledge and data engineering 31(12), 2346–2363 (2018) Novac et al. [2020] Novac, P.-E., Castagnetti, A., Russo, A., Miramond, B., Pegatoquet, A., Verdier, F.: Toward unsupervised human activity recognition on microcontroller units. In: 2020 23rd Euromicro Conference on Digital System Design (DSD), pp. 542–550 (2020). IEEE Ravaglia et al. [2021] Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., Zhang, G.: Learning under concept drift: A review. IEEE transactions on knowledge and data engineering 31(12), 2346–2363 (2018) Novac et al. [2020] Novac, P.-E., Castagnetti, A., Russo, A., Miramond, B., Pegatoquet, A., Verdier, F.: Toward unsupervised human activity recognition on microcontroller units. In: 2020 23rd Euromicro Conference on Digital System Design (DSD), pp. 542–550 (2020). IEEE Ravaglia et al. [2021] Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Novac, P.-E., Castagnetti, A., Russo, A., Miramond, B., Pegatoquet, A., Verdier, F.: Toward unsupervised human activity recognition on microcontroller units. In: 2020 23rd Euromicro Conference on Digital System Design (DSD), pp. 542–550 (2020). IEEE Ravaglia et al. [2021] Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021)
- Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., Zhang, G.: Learning under concept drift: A review. IEEE transactions on knowledge and data engineering 31(12), 2346–2363 (2018) Novac et al. [2020] Novac, P.-E., Castagnetti, A., Russo, A., Miramond, B., Pegatoquet, A., Verdier, F.: Toward unsupervised human activity recognition on microcontroller units. In: 2020 23rd Euromicro Conference on Digital System Design (DSD), pp. 542–550 (2020). IEEE Ravaglia et al. [2021] Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Novac, P.-E., Castagnetti, A., Russo, A., Miramond, B., Pegatoquet, A., Verdier, F.: Toward unsupervised human activity recognition on microcontroller units. In: 2020 23rd Euromicro Conference on Digital System Design (DSD), pp. 542–550 (2020). IEEE Ravaglia et al. [2021] Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021)
- Novac, P.-E., Castagnetti, A., Russo, A., Miramond, B., Pegatoquet, A., Verdier, F.: Toward unsupervised human activity recognition on microcontroller units. In: 2020 23rd Euromicro Conference on Digital System Design (DSD), pp. 542–550 (2020). IEEE Ravaglia et al. [2021] Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021)
- Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., Benini, L.: A tinyml platform for on-device continual learning with quantized latent replays. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11(4), 789–802 (2021) Belacel et al. [2022] Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021)
- Belacel, N., Richard, R., Xu, Z.M.: An lstm encoder-decoder approach for unsupervised online anomaly detection in machine learning packages for streaming data. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3348–3357 (2022). IEEE Munir et al. [2019] Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021)
- Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019) Vázquez et al. [2023] Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021)
- Vázquez, F.I., Hartl, A., Zseby, T., Zimek, A.: Anomaly detection in streaming data: A comparison and evaluation study. Expert Systems with Applications 233, 120994 (2023) Andrade et al. [2023] Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021)
- Andrade, P., Silva, I., Diniz, M., Flores, T., Costa, D.G., Soares, E.: Online processing of vehicular data on the edge through an unsupervised tinyml regression technique. ACM Transactions on Embedded Computing Systems (2023) Angelov [2014] Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021)
- Angelov, P.: Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 1–8 (2014). IEEE Bezerra et al. [2020] Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021)
- Bezerra, C.G., Costa, B.S.J., Guedes, L.A., Angelov, P.P.: An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Information Sciences 518, 13–28 (2020) Oikarinen et al. [2021] Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021)
- Oikarinen, E., Tiittanen, H., Henelius, A., Puolamäki, K.: Detecting virtual concept drift of regressors without ground truth values. Data Min. Knowl. Discov. 35(3), 726–747 (2021) https://doi.org/10.1007/s10618-021-00739-7 James et al. [2021] James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021)
- James, G., Witten, D., Hastie, T., Tibshirani, R.: Linear Regression. In: An Introduction to Statistical Learning: with Applications in R, pp. 59–128. Springer, New York, NY (2021) Zdaniuk [2014] Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021)
- Zdaniuk, B.: Ordinary Least-Squares (OLS) Model. In: Michalos, A.C. (ed.) Encyclopedia of Quality of Life and Well-Being Research, pp. 4515–4517. Springer, Dordrecht (2014) Vito [2016] Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021)
- Vito, S.: Air Quality. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C59K5F (2016) Yeh [2007] Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021)
- Yeh, I.-C.: Concrete Compressive Strength. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5PK67 (2007) Rana [2013] Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021)
- Rana, P.: Physicochemical Properties of Protein Tertiary Structure. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5QW3H (2013) [23] Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021)
- Gas Turbine CO and NOx Emission Data Set. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WC95 (2019) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021)
- Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830 (2011) McKinney et al. [2010] McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021)
- McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010). Austin, TX Hunter [2007] Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021)
- Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering 9(03), 90–95 (2007) Montiel et al. [2021] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021) Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021)
- Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python. The Journal of Machine Learning Research 22(1), 4945–4952 (2021)
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.