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

• URL: http://arxiv.org/abs/2504.08940v1
• Date: Fri, 11 Apr 2025 19:43:11 GMT
• Title: Combining Forecasts using Meta-Learning: A Comparative Study for Complex Seasonality
• Authors: Grzegorz Dudek,
• Abstract summary: We investigate meta-learning for combining forecasts generated by models of different types.<n>We use linear regression, $k$-nearest neighbors, multilayer perceptron, random forest, and long short-term memory as meta-learners.
• Score: 1.6317061277457001
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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