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RPMixer: Shaking Up Time Series Forecasting with Random Projections for Large Spatial-Temporal Data (2402.10487v4)

Published 16 Feb 2024 in cs.LG and cs.AI

Abstract: Spatial-temporal forecasting systems play a crucial role in addressing numerous real-world challenges. In this paper, we investigate the potential of addressing spatial-temporal forecasting problems using general time series forecasting models, i.e., models that do not leverage the spatial relationships among the nodes. We propose a all-Multi-Layer Perceptron (all-MLP) time series forecasting architecture called RPMixer. The all-MLP architecture was chosen due to its recent success in time series forecasting benchmarks. Furthermore, our method capitalizes on the ensemble-like behavior of deep neural networks, where each individual block within the network behaves like a base learner in an ensemble model, particularly when identity mapping residual connections are incorporated. By integrating random projection layers into our model, we increase the diversity among the blocks' outputs, thereby improving the overall performance of the network. Extensive experiments conducted on the largest spatial-temporal forecasting benchmark datasets demonstrate that the proposed method outperforms alternative methods, including both spatial-temporal graph models and general forecasting models.

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(2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chiheb Trabelsi, Olexa Bilaniuk, Ying Zhang, Dmitriy Serdyuk, Sandeep Subramanian, João Felipe Santos, Soroush Mehri, Negar Rostamzadeh, Yoshua Bengio, and Christopher J Pal. 2018. Deep Complex Networks. arXiv:1705.09792 [cs.NE] Ulyanov et al. (2018) Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky. 2018. Deep image prior. In Proceedings of the IEEE conference on computer vision and pattern recognition. 9446–9454. Vaswani et al. (2017) Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017). Veit et al. (2016) Andreas Veit, Michael J Wilber, and Serge Belongie. 2016. Residual networks behave like ensembles of relatively shallow networks. Advances in neural information processing systems 29 (2016). Veličković et al. (2017) Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017). Wu et al. (2021) Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long. 2021. Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. Advances in Neural Information Processing Systems 34 (2021), 22419–22430. Wu et al. (2019) Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019. Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019). Yan et al. (2018) Sijie Yan, Yuanjun Xiong, and Dahua Lin. 2018. Spatial temporal graph convolutional networks for skeleton-based action recognition. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yao et al. (2018) Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yeh et al. (2023a) Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky. 2018. Deep image prior. In Proceedings of the IEEE conference on computer vision and pattern recognition. 9446–9454. Vaswani et al. (2017) Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017). Veit et al. (2016) Andreas Veit, Michael J Wilber, and Serge Belongie. 2016. Residual networks behave like ensembles of relatively shallow networks. Advances in neural information processing systems 29 (2016). Veličković et al. (2017) Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017). Wu et al. (2021) Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long. 2021. Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. Advances in Neural Information Processing Systems 34 (2021), 22419–22430. Wu et al. (2019) Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019. Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019). Yan et al. (2018) Sijie Yan, Yuanjun Xiong, and Dahua Lin. 2018. Spatial temporal graph convolutional networks for skeleton-based action recognition. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yao et al. (2018) Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yeh et al. (2023a) Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017). Veit et al. (2016) Andreas Veit, Michael J Wilber, and Serge Belongie. 2016. Residual networks behave like ensembles of relatively shallow networks. Advances in neural information processing systems 29 (2016). Veličković et al. (2017) Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017). Wu et al. (2021) Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long. 2021. Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. Advances in Neural Information Processing Systems 34 (2021), 22419–22430. Wu et al. (2019) Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019. Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019). Yan et al. (2018) Sijie Yan, Yuanjun Xiong, and Dahua Lin. 2018. Spatial temporal graph convolutional networks for skeleton-based action recognition. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yao et al. (2018) Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yeh et al. (2023a) Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. 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In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yao et al. (2018) Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yeh et al. (2023a) Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. 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In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yao et al. (2018) Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yeh et al. (2023a) Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. 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In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Sijie Yan, Yuanjun Xiong, and Dahua Lin. 2018. Spatial temporal graph convolutional networks for skeleton-based action recognition. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yao et al. (2018) Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yeh et al. (2023a) Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yeh et al. (2023a) Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. 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In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86.
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In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky. 2018. Deep image prior. In Proceedings of the IEEE conference on computer vision and pattern recognition. 9446–9454. Vaswani et al. (2017) Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017). Veit et al. (2016) Andreas Veit, Michael J Wilber, and Serge Belongie. 2016. Residual networks behave like ensembles of relatively shallow networks. Advances in neural information processing systems 29 (2016). Veličković et al. (2017) Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017). Wu et al. (2021) Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long. 2021. Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. Advances in Neural Information Processing Systems 34 (2021), 22419–22430. Wu et al. (2019) Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019. Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019). Yan et al. (2018) Sijie Yan, Yuanjun Xiong, and Dahua Lin. 2018. Spatial temporal graph convolutional networks for skeleton-based action recognition. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yao et al. (2018) Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yeh et al. (2023a) Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017). Veit et al. (2016) Andreas Veit, Michael J Wilber, and Serge Belongie. 2016. Residual networks behave like ensembles of relatively shallow networks. Advances in neural information processing systems 29 (2016). Veličković et al. (2017) Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017). Wu et al. (2021) Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long. 2021. Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. Advances in Neural Information Processing Systems 34 (2021), 22419–22430. Wu et al. (2019) Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019. Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019). Yan et al. (2018) Sijie Yan, Yuanjun Xiong, and Dahua Lin. 2018. Spatial temporal graph convolutional networks for skeleton-based action recognition. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yao et al. (2018) Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yeh et al. (2023a) Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. 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In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long. 2021. Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. Advances in Neural Information Processing Systems 34 (2021), 22419–22430. Wu et al. (2019) Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019. Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019). Yan et al. (2018) Sijie Yan, Yuanjun Xiong, and Dahua Lin. 2018. Spatial temporal graph convolutional networks for skeleton-based action recognition. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yao et al. (2018) Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yeh et al. (2023a) Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. 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In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019. Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019). Yan et al. (2018) Sijie Yan, Yuanjun Xiong, and Dahua Lin. 2018. Spatial temporal graph convolutional networks for skeleton-based action recognition. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yao et al. (2018) Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. 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In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Sijie Yan, Yuanjun Xiong, and Dahua Lin. 2018. Spatial temporal graph convolutional networks for skeleton-based action recognition. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yao et al. (2018) Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yeh et al. (2023a) Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yeh et al. (2023a) Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. 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(2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. 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In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yao et al. (2018) Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yeh et al. (2023a) Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long. 2021. Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. Advances in Neural Information Processing Systems 34 (2021), 22419–22430. Wu et al. (2019) Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019. Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019). Yan et al. (2018) Sijie Yan, Yuanjun Xiong, and Dahua Lin. 2018. Spatial temporal graph convolutional networks for skeleton-based action recognition. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yao et al. (2018) Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yeh et al. (2023a) Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019. Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019). Yan et al. (2018) Sijie Yan, Yuanjun Xiong, and Dahua Lin. 2018. Spatial temporal graph convolutional networks for skeleton-based action recognition. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yao et al. (2018) Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yeh et al. (2023a) Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Sijie Yan, Yuanjun Xiong, and Dahua Lin. 2018. Spatial temporal graph convolutional networks for skeleton-based action recognition. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yao et al. (2018) Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yeh et al. (2023a) Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yeh et al. (2023a) Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. 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(2018) Sijie Yan, Yuanjun Xiong, and Dahua Lin. 2018. Spatial temporal graph convolutional networks for skeleton-based action recognition. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yao et al. (2018) Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yeh et al. (2023a) Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Andreas Veit, Michael J Wilber, and Serge Belongie. 2016. Residual networks behave like ensembles of relatively shallow networks. Advances in neural information processing systems 29 (2016). Veličković et al. (2017) Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017). Wu et al. (2021) Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long. 2021. Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. Advances in Neural Information Processing Systems 34 (2021), 22419–22430. Wu et al. (2019) Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019. Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019). Yan et al. (2018) Sijie Yan, Yuanjun Xiong, and Dahua Lin. 2018. Spatial temporal graph convolutional networks for skeleton-based action recognition. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yao et al. (2018) Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yeh et al. (2023a) Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017). Wu et al. (2021) Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long. 2021. Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. Advances in Neural Information Processing Systems 34 (2021), 22419–22430. Wu et al. (2019) Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019. Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019). Yan et al. (2018) Sijie Yan, Yuanjun Xiong, and Dahua Lin. 2018. Spatial temporal graph convolutional networks for skeleton-based action recognition. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yao et al. (2018) Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yeh et al. (2023a) Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long. 2021. Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. Advances in Neural Information Processing Systems 34 (2021), 22419–22430. Wu et al. (2019) Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019. Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019). Yan et al. (2018) Sijie Yan, Yuanjun Xiong, and Dahua Lin. 2018. Spatial temporal graph convolutional networks for skeleton-based action recognition. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yao et al. (2018) Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yeh et al. (2023a) Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019. Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019). Yan et al. (2018) Sijie Yan, Yuanjun Xiong, and Dahua Lin. 2018. Spatial temporal graph convolutional networks for skeleton-based action recognition. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yao et al. (2018) Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yeh et al. (2023a) Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Sijie Yan, Yuanjun Xiong, and Dahua Lin. 2018. Spatial temporal graph convolutional networks for skeleton-based action recognition. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yao et al. (2018) Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yeh et al. (2023a) Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yeh et al. (2023a) Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. 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Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. 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(2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. 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In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. 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(2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017). Wu et al. (2021) Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long. 2021. Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. Advances in Neural Information Processing Systems 34 (2021), 22419–22430. Wu et al. (2019) Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019. Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019). Yan et al. (2018) Sijie Yan, Yuanjun Xiong, and Dahua Lin. 2018. Spatial temporal graph convolutional networks for skeleton-based action recognition. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yao et al. (2018) Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yeh et al. (2023a) Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long. 2021. Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. Advances in Neural Information Processing Systems 34 (2021), 22419–22430. Wu et al. (2019) Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019. Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019). Yan et al. (2018) Sijie Yan, Yuanjun Xiong, and Dahua Lin. 2018. Spatial temporal graph convolutional networks for skeleton-based action recognition. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yao et al. (2018) Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yeh et al. (2023a) Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019. Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019). Yan et al. (2018) Sijie Yan, Yuanjun Xiong, and Dahua Lin. 2018. Spatial temporal graph convolutional networks for skeleton-based action recognition. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yao et al. (2018) Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yeh et al. (2023a) Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Sijie Yan, Yuanjun Xiong, and Dahua Lin. 2018. Spatial temporal graph convolutional networks for skeleton-based action recognition. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yao et al. (2018) Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yeh et al. (2023a) Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yeh et al. (2023a) Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. 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Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. 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Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. 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(2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. 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Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86.
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Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. 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Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long. 2021. Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. Advances in Neural Information Processing Systems 34 (2021), 22419–22430. Wu et al. (2019) Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019. Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019). Yan et al. (2018) Sijie Yan, Yuanjun Xiong, and Dahua Lin. 2018. Spatial temporal graph convolutional networks for skeleton-based action recognition. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yao et al. (2018) Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yeh et al. (2023a) Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. 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(2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yeh et al. (2023a) Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. 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Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. 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In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86.
  41. Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. Advances in Neural Information Processing Systems 34 (2021), 22419–22430. Wu et al. (2019) Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019. Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019). Yan et al. (2018) Sijie Yan, Yuanjun Xiong, and Dahua Lin. 2018. Spatial temporal graph convolutional networks for skeleton-based action recognition. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yao et al. (2018) Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yeh et al. (2023a) Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019. Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019). Yan et al. (2018) Sijie Yan, Yuanjun Xiong, and Dahua Lin. 2018. Spatial temporal graph convolutional networks for skeleton-based action recognition. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yao et al. (2018) Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yeh et al. (2023a) Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Sijie Yan, Yuanjun Xiong, and Dahua Lin. 2018. Spatial temporal graph convolutional networks for skeleton-based action recognition. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yao et al. (2018) Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yeh et al. (2023a) Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yeh et al. (2023a) Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. 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Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. 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In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. 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(2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. 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(2018) Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yeh et al. (2023a) Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yeh et al. (2023a) Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. 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Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86.
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Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yeh et al. (2023a) Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86.
  44. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32. Yeh et al. (2023a) Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023a. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86.
  45. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400–4404. Yeh et al. (2022a) Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022a. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391–4401. Yeh et al. (2017) Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. 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In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. 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Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. 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Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86.
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(2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86.
  47. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565–574. Yeh and Keogh (2016) Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD’16 Challenge. https://github.com/mcyeh/aaltd16_fusion. Yeh et al. (2023b) Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023b. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023). Yeh et al. (2022b) Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. 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Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022b. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. 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Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. 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Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. 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Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. 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Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181–189. Yeh et al. (2016) Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322. Yeh (2018) Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside. Yu et al. (2017) Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. 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(2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. 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Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). Zeng et al. (2023) Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 11121–11128. Zhang et al. (2023) Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. 2023. MLPST: MLP is All You Need for Spatio-Temporal Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3381–3390. Zhou et al. (2021) Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86. Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 11106–11115. Zhou et al. (2022) Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268–27286. Zimmerman et al. (2019) Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. 2019. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. 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  58. Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In Proceedings of the ACM Symposium on Cloud Computing. 74–86.
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Authors (12)
  1. Chin-Chia Michael Yeh (43 papers)
  2. Yujie Fan (25 papers)
  3. Xin Dai (27 papers)
  4. Vivian Lai (28 papers)
  5. Prince Osei Aboagye (3 papers)
  6. Junpeng Wang (53 papers)
  7. Huiyuan Chen (43 papers)
  8. Yan Zheng (102 papers)
  9. Zhongfang Zhuang (32 papers)
  10. Liang Wang (512 papers)
  11. Wei Zhang (1489 papers)
  12. Uday Singh Saini (12 papers)
Citations (5)
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