Winner-takes-all for Multivariate Probabilistic Time Series Forecasting

Adrien Cortes, Remi Rehm, Victor Letzelter
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:11288-11312, 2025.

Abstract

We introduce $\texttt{TimeMCL}$, a method leveraging the Multiple Choice Learning (MCL) paradigm to forecast multiple plausible time series futures. Our approach employs a neural network with multiple heads and utilizes the Winner-Takes-All (WTA) loss to promote diversity among predictions. MCL has recently gained attention due to its simplicity and ability to address ill-posed and ambiguous tasks. We propose an adaptation of this framework for time-series forecasting, presenting it as an efficient method to predict diverse futures, which we relate to its implicit quantization objective. We provide insights into our approach using synthetic data and evaluate it on real-world time series, demonstrating its promising performance at a light computational cost.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-cortes25b, title = {Winner-takes-all for Multivariate Probabilistic Time Series Forecasting}, author = {Cortes, Adrien and Rehm, Remi and Letzelter, Victor}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {11288--11312}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/cortes25b/cortes25b.pdf}, url = {https://proceedings.mlr.press/v267/cortes25b.html}, abstract = {We introduce $\texttt{TimeMCL}$, a method leveraging the Multiple Choice Learning (MCL) paradigm to forecast multiple plausible time series futures. Our approach employs a neural network with multiple heads and utilizes the Winner-Takes-All (WTA) loss to promote diversity among predictions. MCL has recently gained attention due to its simplicity and ability to address ill-posed and ambiguous tasks. We propose an adaptation of this framework for time-series forecasting, presenting it as an efficient method to predict diverse futures, which we relate to its implicit quantization objective. We provide insights into our approach using synthetic data and evaluate it on real-world time series, demonstrating its promising performance at a light computational cost.} }
Endnote
%0 Conference Paper %T Winner-takes-all for Multivariate Probabilistic Time Series Forecasting %A Adrien Cortes %A Remi Rehm %A Victor Letzelter %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-cortes25b %I PMLR %P 11288--11312 %U https://proceedings.mlr.press/v267/cortes25b.html %V 267 %X We introduce $\texttt{TimeMCL}$, a method leveraging the Multiple Choice Learning (MCL) paradigm to forecast multiple plausible time series futures. Our approach employs a neural network with multiple heads and utilizes the Winner-Takes-All (WTA) loss to promote diversity among predictions. MCL has recently gained attention due to its simplicity and ability to address ill-posed and ambiguous tasks. We propose an adaptation of this framework for time-series forecasting, presenting it as an efficient method to predict diverse futures, which we relate to its implicit quantization objective. We provide insights into our approach using synthetic data and evaluate it on real-world time series, demonstrating its promising performance at a light computational cost.
APA
Cortes, A., Rehm, R. & Letzelter, V.. (2025). Winner-takes-all for Multivariate Probabilistic Time Series Forecasting. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:11288-11312 Available from https://proceedings.mlr.press/v267/cortes25b.html.

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