Ensemble Prediction via Covariate-dependent Stacking
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866908559442182144 |
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| author | Wakayama, Tomoya Sugasawa, Shonosuke |
| author_facet | Wakayama, Tomoya Sugasawa, Shonosuke |
| contents | This study proposes a novel approach to ensemble prediction, called "covariate-dependent stacking" (CDST). Unlike traditional stacking and model averaging methods, CDST allows model weights to vary flexibly as a function of covariates, thereby enhancing predictive performance in complex scenarios. We formulate the covariate-dependent weights through combinations of basis functions and estimate them via cross-validation optimization. To analyze the theoretical properties, we establish an oracle inequality regarding the expected loss to be minimized for estimating model weights. Through comprehensive simulation studies and an application to large-scale land price prediction, we demonstrate that the CDST consistently outperforms conventional model averaging methods, particularly on datasets where base models fail to capture the underlying complexity. Our findings suggest that the CDST is especially valuable for, but not limited to, spatio-temporal prediction problems, offering a powerful tool for researchers and practitioners across a wide spectrum of data analysis fields. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_09755 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Ensemble Prediction via Covariate-dependent Stacking Wakayama, Tomoya Sugasawa, Shonosuke Methodology Machine Learning This study proposes a novel approach to ensemble prediction, called "covariate-dependent stacking" (CDST). Unlike traditional stacking and model averaging methods, CDST allows model weights to vary flexibly as a function of covariates, thereby enhancing predictive performance in complex scenarios. We formulate the covariate-dependent weights through combinations of basis functions and estimate them via cross-validation optimization. To analyze the theoretical properties, we establish an oracle inequality regarding the expected loss to be minimized for estimating model weights. Through comprehensive simulation studies and an application to large-scale land price prediction, we demonstrate that the CDST consistently outperforms conventional model averaging methods, particularly on datasets where base models fail to capture the underlying complexity. Our findings suggest that the CDST is especially valuable for, but not limited to, spatio-temporal prediction problems, offering a powerful tool for researchers and practitioners across a wide spectrum of data analysis fields. |
| title | Ensemble Prediction via Covariate-dependent Stacking |
| topic | Methodology Machine Learning |
| url | https://arxiv.org/abs/2408.09755 |