PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Adaptive Ensemble Models of Extreme Learning Machines for Time Series Prediction
Mark van Heeswijk, Yoan Miche, Tiina Lindh-Knuutila, Peter Hilbers, Timo Honkela, Erkki Oja and Amaury Lendasse
In: Artificial Neural Networks – ICANN 2009 Lecture Notes in Computer Science , 5769/2009 . (2009) Springer Berlin / Heidelberg , pp. 305-314. ISBN 0302-9743


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.

PDF - PASCAL Members only - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:6660
Deposited By:Amaury Lendasse
Deposited On:08 March 2010