Adaptive Ensemble Models of Extreme Learning Machines for Time Series Prediction

Authors:
Mark Heeswijk;Yoan Miche;Tiina Lindh-Knuutila;Peter A. Hilbers;Timo Honkela;Erkki Oja;Amaury Lendasse
Affiliations:
Adaptive Informatics Research Centre, Helsinki University of Technology, TKK, Finland 02015 and Eindhoven University of Technology, Eindhoven, The Netherlands 5600 MB;Adaptive Informatics Research Centre, Helsinki University of Technology, TKK, Finland 02015 and INPG Grenoble - Gipsa-Lab, UMR 5216, Grenoble, France 38402;Adaptive Informatics Research Centre, Helsinki University of Technology, TKK, Finland 02015 and International Computer Science Institute of University of California, Berkeley, USA 94704;Eindhoven University of Technology, Eindhoven, The Netherlands 5600 MB;Adaptive Informatics Research Centre, Helsinki University of Technology, TKK, Finland 02015;Adaptive Informatics Research Centre, Helsinki University of Technology, TKK, Finland 02015;Adaptive Informatics Research Centre, Helsinki University of Technology, TKK, Finland 02015
Venue:
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Year:
2009

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Abstract

In this paper, we investigate the application of adaptive ensemble models of Extreme Learning Machines (ELMs) to the problem of one-step ahead prediction in (non)stationary time series. We verify that the method works on stationary time series and test the adaptivity of the ensemble model on a nonstationary time series. In the experiments, we show that the adaptive ensemble model achieves a test error comparable to the best methods, while keeping adaptivity. Moreover, it has low computational cost.