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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2504.08940 |
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| _version_ | 1866910909617668096 |
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| author | Dudek, Grzegorz |
| author_facet | Dudek, Grzegorz |
| contents | In this paper, we investigate meta-learning for combining forecasts generated by models of different types. While typical approaches for combining forecasts involve simple averaging, machine learning techniques enable more sophisticated methods of combining through meta-learning, leading to improved forecasting accuracy. We use linear regression, $k$-nearest neighbors, multilayer perceptron, random forest, and long short-term memory as meta-learners. We define global and local meta-learning variants for time series with complex seasonality and compare meta-learners on multiple forecasting problems, demonstrating their superior performance compared to simple averaging. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_08940 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Combining Forecasts using Meta-Learning: A Comparative Study for Complex Seasonality Dudek, Grzegorz Machine Learning Artificial Intelligence In this paper, we investigate meta-learning for combining forecasts generated by models of different types. While typical approaches for combining forecasts involve simple averaging, machine learning techniques enable more sophisticated methods of combining through meta-learning, leading to improved forecasting accuracy. We use linear regression, $k$-nearest neighbors, multilayer perceptron, random forest, and long short-term memory as meta-learners. We define global and local meta-learning variants for time series with complex seasonality and compare meta-learners on multiple forecasting problems, demonstrating their superior performance compared to simple averaging. |
| title | Combining Forecasts using Meta-Learning: A Comparative Study for Complex Seasonality |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2504.08940 |