Forecasting time series with constraints
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909493994979328 |
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| author | Doumèche, Nathan Bach, Francis Bedek, Éloi Biau, Gérard Boyer, Claire Goude, Yannig |
| author_facet | Doumèche, Nathan Bach, Francis Bedek, Éloi Biau, Gérard Boyer, Claire Goude, Yannig |
| contents | Time series forecasting presents unique challenges that limit the effectiveness of traditional machine learning algorithms. To address these limitations, various approaches have incorporated linear constraints into learning algorithms, such as generalized additive models and hierarchical forecasting. In this paper, we propose a unified framework for integrating and combining linear constraints in time series forecasting. Within this framework, we show that the exact minimizer of the constrained empirical risk can be computed efficiently using linear algebra alone. This approach allows for highly scalable implementations optimized for GPUs. We validate the proposed methodology through extensive benchmarking on real-world tasks, including electricity demand forecasting and tourism forecasting, achieving state-of-the-art performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_10485 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Forecasting time series with constraints Doumèche, Nathan Bach, Francis Bedek, Éloi Biau, Gérard Boyer, Claire Goude, Yannig Machine Learning Artificial Intelligence Statistics Theory Applications Methodology Time series forecasting presents unique challenges that limit the effectiveness of traditional machine learning algorithms. To address these limitations, various approaches have incorporated linear constraints into learning algorithms, such as generalized additive models and hierarchical forecasting. In this paper, we propose a unified framework for integrating and combining linear constraints in time series forecasting. Within this framework, we show that the exact minimizer of the constrained empirical risk can be computed efficiently using linear algebra alone. This approach allows for highly scalable implementations optimized for GPUs. We validate the proposed methodology through extensive benchmarking on real-world tasks, including electricity demand forecasting and tourism forecasting, achieving state-of-the-art performance. |
| title | Forecasting time series with constraints |
| topic | Machine Learning Artificial Intelligence Statistics Theory Applications Methodology |
| url | https://arxiv.org/abs/2502.10485 |