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

Abstract--In this paper, we investigate meta-learning for combining forecasts generated by models of different types . While typical approaches for combining forecasts involve s imple averaging, machine learning techniques enable more sophis ti-cated 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 compl ex seasonality and compare meta-learners on multiple forecas ting problems, demonstrating their superior performance compa red to simple averaging. Ensemble methods are widely recognized as a cornerstone of modern machine learning (ML) [1], commonly used for regression and classification problems. In addition, ensem bling has proven to be a highly effective approach for increasing the predictive power of forecasting models. The ensemble approach in forecasting, which involves combining the predictions of multiple models, can be justified for several reasons. First of all, it usually leads to increased accurac y. Ensemble models often outperform individual models, as the y leverage the strengths of different models and minimize the ir weaknesses. By combining diverse models, the ensemble can produce more accurate predictions by capturing a broader range of patterns and insights from the data. Ensembling als o allows for the incorporation of multiple drivers into the da ta generating process, mitigating uncertainties regarding m odel form and parameter specification [2].