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.
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This work was partially supported by the Slovak Research and Development Agency under the contract APVV-16-0213.
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Lóderer, M., Pavlík, P., Rozinajová, V. (2018). Improving Time Series Prediction via Modification of Dynamic Weighted Majority in Ensemble Learning. In: Yin, H., Camacho, D., Novais, P., Tallón-Ballesteros, A. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2018. IDEAL 2018. Lecture Notes in Computer Science(), vol 11314. Springer, Cham. https://doi.org/10.1007/978-3-030-03493-1_68
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