Multivariate Online Linear Regression for Hierarchical Forecasting
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866929252886118400 |
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| author | Hihat, Massil Garrigos, Guillaume Fermanian, Adeline Bussy, Simon |
| author_facet | Hihat, Massil Garrigos, Guillaume Fermanian, Adeline Bussy, Simon |
| contents | In this paper, we consider a deterministic online linear regression model where we allow the responses to be multivariate. To address this problem, we introduce MultiVAW, a method that extends the well-known Vovk-Azoury-Warmuth algorithm to the multivariate setting, and show that it also enjoys logarithmic regret in time. We apply our results to the online hierarchical forecasting problem and recover an algorithm from this literature as a special case, allowing us to relax the hypotheses usually made for its analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_14578 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multivariate Online Linear Regression for Hierarchical Forecasting Hihat, Massil Garrigos, Guillaume Fermanian, Adeline Bussy, Simon Machine Learning Optimization and Control In this paper, we consider a deterministic online linear regression model where we allow the responses to be multivariate. To address this problem, we introduce MultiVAW, a method that extends the well-known Vovk-Azoury-Warmuth algorithm to the multivariate setting, and show that it also enjoys logarithmic regret in time. We apply our results to the online hierarchical forecasting problem and recover an algorithm from this literature as a special case, allowing us to relax the hypotheses usually made for its analysis. |
| title | Multivariate Online Linear Regression for Hierarchical Forecasting |
| topic | Machine Learning Optimization and Control |
| url | https://arxiv.org/abs/2402.14578 |