Adaptive Ensemble Models of Extreme Learning Machines for Time Series Prediction

Mark Van Heeswijk,Tiina Lindh-Knuutila,Timo Honkela,Amaury Lendasse,Erkki Oja,Yoan Miche,Peter A J Hilbers

doi:10.1007/978-3-642-04277-5_31

Adaptive Ensemble Models of Extreme Learning Machines for Time Series Prediction

Mark Van Heeswijk, Tiina Lindh-Knuutila + Show 5 more

Open Access

https://doi.org/10.1007/978-3-642-04277-5_31

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Publication Date: Jan 1, 2009

Citations: 104

Affiliation: University of Helsinki, Eindhoven University of Technology, International Computer Science Institute, University of California, Berkeley, Grenoble Institute of Technology, Grenoble Images Parole Signal Automatique, Université Grenoble Alpes

#Nonstationary Time Series #Stationary Time Series + Show 8 more

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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.

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