Multi-layer Stack Ensembles for Time Series Forecasting
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912718805532672 |
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| author | Bosch, Nathanael Shchur, Oleksandr Erickson, Nick Bohlke-Schneider, Michael Türkmen, Caner |
| author_facet | Bosch, Nathanael Shchur, Oleksandr Erickson, Nick Bohlke-Schneider, Michael Türkmen, Caner |
| contents | Ensembling is a powerful technique for improving the accuracy of machine learning models, with methods like stacking achieving strong results in tabular tasks. In time series forecasting, however, ensemble methods remain underutilized, with simple linear combinations still considered state-of-the-art. In this paper, we systematically explore ensembling strategies for time series forecasting. We evaluate 33 ensemble models -- both existing and novel -- across 50 real-world datasets. Our results show that stacking consistently improves accuracy, though no single stacker performs best across all tasks. To address this, we propose a multi-layer stacking framework for time series forecasting, an approach that combines the strengths of different stacker models. We demonstrate that this method consistently provides superior accuracy across diverse forecasting scenarios. Our findings highlight the potential of stacking-based methods to improve AutoML systems for time series forecasting. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_15350 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-layer Stack Ensembles for Time Series Forecasting Bosch, Nathanael Shchur, Oleksandr Erickson, Nick Bohlke-Schneider, Michael Türkmen, Caner Machine Learning Ensembling is a powerful technique for improving the accuracy of machine learning models, with methods like stacking achieving strong results in tabular tasks. In time series forecasting, however, ensemble methods remain underutilized, with simple linear combinations still considered state-of-the-art. In this paper, we systematically explore ensembling strategies for time series forecasting. We evaluate 33 ensemble models -- both existing and novel -- across 50 real-world datasets. Our results show that stacking consistently improves accuracy, though no single stacker performs best across all tasks. To address this, we propose a multi-layer stacking framework for time series forecasting, an approach that combines the strengths of different stacker models. We demonstrate that this method consistently provides superior accuracy across diverse forecasting scenarios. Our findings highlight the potential of stacking-based methods to improve AutoML systems for time series forecasting. |
| title | Multi-layer Stack Ensembles for Time Series Forecasting |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2511.15350 |