Sliding-Window Signatures for Time Series: Application to Electricity Demand Forecasting
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866917011935723520 |
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| author | Drobac, Nina Brégère, Margaux de Vilmarest, Joseph Wintenberger, Olivier |
| author_facet | Drobac, Nina Brégère, Margaux de Vilmarest, Joseph Wintenberger, Olivier |
| contents | Nonlinear and delayed effects of covariates often render time series forecasting challenging. To this end, we propose a novel forecasting framework based on ridge regression with signature features calculated on sliding windows. These features capture complex temporal dynamics without relying on learned or hand-crafted representations. Focusing on the discrete-time setting, we establish theoretical guarantees, namely universality of approximation and stationarity of signatures. We introduce an efficient sequential algorithm for computing signatures on sliding windows. The method is evaluated on both synthetic and real electricity demand data. Results show that signature features effectively encode temporal and nonlinear dependencies, yielding accurate forecasts competitive with those based on expert knowledge. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_12337 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sliding-Window Signatures for Time Series: Application to Electricity Demand Forecasting Drobac, Nina Brégère, Margaux de Vilmarest, Joseph Wintenberger, Olivier Methodology Machine Learning Nonlinear and delayed effects of covariates often render time series forecasting challenging. To this end, we propose a novel forecasting framework based on ridge regression with signature features calculated on sliding windows. These features capture complex temporal dynamics without relying on learned or hand-crafted representations. Focusing on the discrete-time setting, we establish theoretical guarantees, namely universality of approximation and stationarity of signatures. We introduce an efficient sequential algorithm for computing signatures on sliding windows. The method is evaluated on both synthetic and real electricity demand data. Results show that signature features effectively encode temporal and nonlinear dependencies, yielding accurate forecasts competitive with those based on expert knowledge. |
| title | Sliding-Window Signatures for Time Series: Application to Electricity Demand Forecasting |
| topic | Methodology Machine Learning |
| url | https://arxiv.org/abs/2510.12337 |