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End-to-End Learning of Coherent Probabilistic Forecasts for Hierarchical Time Series

Rangapuram, Syama Sundar and Werner, Lucien D. and Benidis, Konstantinos and Mercado, Pedro and Gasthaus, Jan and Januschowski, Tim (2021) End-to-End Learning of Coherent Probabilistic Forecasts for Hierarchical Time Series. Proceedings of Machine Learning Research, 139 . pp. 8832-8843. ISSN 2640-3498.

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This paper presents a novel approach for hierarchical time series forecasting that produces coherent, probabilistic forecasts without requiring any explicit post-processing reconciliation. Unlike the state-of-the-art, the proposed method simultaneously learns from all time series in the hierarchy and incorporates the reconciliation step into a single trainable model. This is achieved by applying the reparameterization trick and casting reconciliation as an optimization problem with a closed-form solution. These model features make end-to-end learning of hierarchical forecasts possible, while accomplishing the challenging task of generating forecasts that are both probabilistic and coherent. Importantly, our approach also accommodates general aggregation constraints including grouped and temporal hierarchies. An extensive empirical evaluation on real-world hierarchical datasets demonstrates the advantages of the proposed approach over the state-of-the-art.

Item Type:Article
Related URLs:
URLURL TypeDescription
Rangapuram, Syama Sundar0000-0002-9357-0154
Gasthaus, Jan0000-0002-2007-773X
Januschowski, Tim0000-0002-6475-1626
Additional Information:© 2021 by the author(s). The authors thank Souhaib Ben Taieb for insightful discussions on this topic and his timely help in reproducing results with PERMBU-MINT. The authors are also grateful for being able to build on the work of Valentin Flunkert and David Salinas in both concepts and code.
Record Number:CaltechAUTHORS:20220622-211540696
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Official Citation:Rangapuram, S.S., Werner, L.D., Benidis, K., Mercado, P., Gasthaus, J., Januschowski, T. (2021). End-to-End Learning of Coherent Probabilistic Forecasts for Hierarchical Time Series. Proceedings of the 38th International Conference on Machine Learning, in Proceedings of Machine Learning Research, 139:8832-8843
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:115236
Deposited By: Tony Diaz
Deposited On:28 Jun 2022 19:28
Last Modified:28 Jun 2022 19:28

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