🤖 AI Summary
This paper addresses the limited predictive accuracy of multi-model ensembling in complex seasonal time series forecasting. We propose a meta-learning-based dynamic weighted ensemble framework. Methodologically, we systematically compare five meta-learners—linear regression, k-nearest neighbors (KNN), multilayer perceptron (MLP), random forest, and LSTM—under both global and local paradigms, leveraging temporally informed feature engineering to construct meta-features. Our key contribution is the first systematic empirical evaluation of meta-learning-based ensemble strategies specifically for complex seasonal forecasting scenarios. Experimental results across multiple benchmark datasets demonstrate that all proposed meta-learning ensembles significantly outperform simple averaging, achieving average MAE reductions of 12.7%–23.4%. These gains confirm the framework’s superior adaptability to multi-scale, non-stationary seasonal patterns and its strong generalization capability.
📝 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, knearest 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.