Winner-takes-all for Multivariate Probabilistic Time Series Forecasting
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918121931014144 |
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| author | Cortés, Adrien Rehm, Rémi Letzelter, Victor |
| author_facet | Cortés, Adrien Rehm, Rémi Letzelter, Victor |
| contents | We introduce 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_05515 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Winner-takes-all for Multivariate Probabilistic Time Series Forecasting Cortés, Adrien Rehm, Rémi Letzelter, Victor Machine Learning Artificial Intelligence We introduce 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. |
| title | Winner-takes-all for Multivariate Probabilistic Time Series Forecasting |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2506.05515 |