Improving Time Series Prediction via Modification of Dynamic Weighted Majority in Ensemble Learning

Abstract

In this paper, we explore how the modified Dynamic Weighted Majority (DWM) method of ensemble learning can enhance time series prediction. DWM approach was originally introduced as a method to combine predictions of multiple classifiers. In our approach, we propose its modification to solve the regression problems which are based on using differing features to further improve the accuracy of the ensemble. The proposed method is then tested in the domain of energy consumption forecasting.