Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
124 tokens/sec
GPT-4o
8 tokens/sec
Gemini 2.5 Pro Pro
47 tokens/sec
o3 Pro
5 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Hierarchical Neural Additive Models for Interpretable Demand Forecasts (2404.04070v1)

Published 5 Apr 2024 in cs.LG and cs.HC

Abstract: Demand forecasts are the crucial basis for numerous business decisions, ranging from inventory management to strategic facility planning. While ML approaches offer accuracy gains, their interpretability and acceptance are notoriously lacking. Addressing this dilemma, we introduce Hierarchical Neural Additive Models for time series (HNAM). HNAM expands upon Neural Additive Models (NAM) by introducing a time-series specific additive model with a level and interacting covariate components. Covariate interactions are only allowed according to a user-specified interaction hierarchy. For example, weekday effects may be estimated independently of other covariates, whereas a holiday effect may depend on the weekday and an additional promotion may depend on both former covariates that are lower in the interaction hierarchy. Thereby, HNAM yields an intuitive forecasting interface in which analysts can observe the contribution for each known covariate. We evaluate the proposed approach and benchmark its performance against other state-of-the-art machine learning and statistical models extensively on real-world retail data. The results reveal that HNAM offers competitive prediction performance whilst providing plausible explanations.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (41)
  1. Peeking inside the black-box: a survey on explainable artificial intelligence (xai). IEEE access, 6:52138–52160.
  2. Neural additive models: Interpretable machine learning with neural nets. Advances in neural information processing systems, 34:4699–4711.
  3. Beitner, J. (2020). GitHub - jdb78/pytorch-forecasting: Time series forecasting with PyTorch — github.com. https://github.com/jdb78/pytorch-forecasting. Accessed: 2024-04-03.
  4. A systematic review of algorithm aversion in augmented decision making. Journal of Behavioral Decision Making, 33(2):220–239.
  5. Accuracy of judgmental forecasting of time series. Decision Sciences, 16(2):153–160.
  6. Dawes, R. M. (1979). The robust beauty of improper linear models in decision making. American Psychologist, 34:571–582.
  7. People reject algorithms in uncertain decision domains because they have diminishing sensitivity to forecasting error. Psychological Science, 31(10):1302–1314.
  8. Algorithm aversion: people erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General, 144(1):114.
  9. The use of non-time series information in sales forecasting: A case study. Journal of Forecasting, 7(3):201–211.
  10. Edmundson, R. (1990). Decomposition; a strategy for judgemental forecasting. Journal of Forecasting, 9(4):305–314.
  11. Favorita, C. (2018). Corporacion favorita grocery sales forecasting competition. https://www.kaggle.com/c/favorita-grocery-sales-forecasting/. Accessed: 2024-04-03.
  12. Solving the data-driven newsvendor with attention to time. In ECIS 2023 Proceedings.
  13. Stability in the inefficient use of forecasting systems: A case study in a supply chain company. International Journal of Forecasting, 37(2):1031–1046.
  14. Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning. International Journal of Forecasting, 25(1):3–23.
  15. StatsForecast: Lightning fast forecasting with statistical and econometric models. PyCon Salt Lake City, Utah, US 2022.
  16. Forecast adjustments during post-promotional periods. European Journal of Operational Research, 300(2):461–472.
  17. Complex seasonality. In Forecasting: Principles and Practice. OTexts, 3 edition. Online edition.
  18. Neural additive time-series models: Explainable deep learning for multivariate time-series prediction. Expert Systems with Applications, 228:120307.
  19. An examination of the accuracy of judgmental extrapolation of time series. International Journal of Forecasting, 1(1):25–35.
  20. The risk of algorithm transparency: How algorithm complexity drives the effects on the use of advice. Production and Operations Management, 31(9):3419–3434.
  21. Temporal fusion transformers for interpretable multi-horizon time series forecasting. International Journal of Forecasting, 37(4):1748–1764.
  22. A unified approach to interpreting model predictions. Advances in neural information processing systems, 30.
  23. The accuracy of extrapolation (time series) methods: Results of a forecasting competition. Journal of Forecasting, 1(2):111–153.
  24. Statistical and machine learning forecasting methods: Concerns and ways forward. PLOS ONE, 13(3):e0194889.
  25. M5 accuracy competition: Results, findings, and conclusions. International Journal of Forecasting, 38(4):1346–1364.
  26. Structuring and integrating human knowledge in demand forecasting: a judgemental adjustment approach. Production Planning and Control, 21(4):399–412.
  27. Meehl, P. E. (1954). Clinical versus statistical prediction: A theoretical analysis and a review of the evidence. University of Minnesota Press, Minneapolis.
  28. Mello, J. (2009). The impact of sales forecast game playing on supply chains. Foresight: The International Journal of Applied Forecasting, 13:13–22.
  29. Sales forecasting management: a demand management approach. Sage Publications.
  30. Telling more than we can know: Verbal reports on mental processes. Psychological Review, 84(3):231.
  31. Understanding algorithm aversion: When is advice from automation discounted? Journal of Forecasting, 36(6):691–702.
  32. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144.
  33. Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5):206–215.
  34. Deepar: Probabilistic forecasting with autoregressive recurrent networks. International Journal of Forecasting, 36(3):1181–1191.
  35. The need for contextual and technical knowledge in judgmental forecasting. Journal of Behavioral Decision Making, 5(1):39–52.
  36. GitHub - facebook/prophet: Prophet: Automatic forecasting procedure — github.com. https://github.com/facebook/prophet. Accessed: 2024-04-03.
  37. Forecasting at scale. The American Statistician, 72(1):37–45.
  38. Analysis of judgmental adjustments in the presence of promotions. International Journal of Forecasting, 29(2):234–243.
  39. University of Nicosia (2020). M5 forecasting - accuracy: Estimate the unit sales of walmart retail goods. https://www.kaggle.com/c/m5-forecasting-accuracy. Accessed: 2024-04-03.
  40. Attention is all you need. Advances in neural information processing systems, 30.
  41. Designing transparency for effective human-ai collaboration. Information Systems Frontiers, 24(3):877–895.
Citations (1)

Summary

We haven't generated a summary for this paper yet.

X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets