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arXiv · 2504.08940

Combining Forecasts using Meta-Learning: A Comparative Study for Complex Seasonality

Grzegorz Dudek
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Abstract

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.

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BibTeXRIS

Grzegorz Dudek. 2025-04-11. Combining Forecasts using Meta-Learning: A Comparative Study for Complex Seasonality. https://doi.org/10.1109/dsaa60987.2023.10302585

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