A Comparative Analysis of Combination Operators in Heterogeneous Ensembles for One-Step-Ahead Time Series Forecasting - Albuquerque, Rodolfo Viegas de; Oliveira, João Fausto Lorenzato de (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), 2025)
This study investigates the efficacy of four combination operators in heterogeneous ensembles for one-step-ahead time series forecasting tasks: simple mean, median, stacking with Support Vector Regression (SVR) as a metamodel, and weighted average. Nine machine learning and statistical models were trained and subsequently their outcomes were averaged. The results show that the simple mean operator outperforms the median operator in terms of RMSE and SMAPE, yielding an average error of 15.888% and 17.791%, respectively, when compared to individual models and previous iterations of themselves. In contrast, the novel weighting average operator yielded the lowest average error values for both metrics, indicating potential for further enhancements and research. In addition to the increase in accuracy, the data analysis reveals that for monthly time series and within a forecasting framework, there is a tendency for the standard deviation to decrease as more models are incorporated.