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

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.

@article{dudek2025_2504.08940,
  title={ Combining Forecasts using Meta-Learning: A Comparative Study for Complex Seasonality },
  author={ Grzegorz Dudek },
  journal={arXiv preprint arXiv:2504.08940},
  year={ 2025 }
}