Radial basis function nets for time series prediction

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Authors: Bouchachia, A.

Journal: International Journal of Computational Intelligence Systems

Volume: 2

Issue: 2

Pages: 147-157

eISSN: 1875-6883

ISSN: 1875-6891

DOI: 10.1080/18756891.2009.9727650

This paper introduces a novel ensemble learning approach based on recurrent radial basis function networks (RRBFN) for time series prediction with the aim of increasing the prediction accuracy. Standing for the base learner in this ensemble, the adaptive recurrent network proposed is based on the nonlinear autoregressive with exogenous input model (NARX) and works according to a multi-step (MS) prediction regime. The ensemble learning technique combines various MSNARX-based RRBFNs which differ in the set of controlling parameters. The evaluation of the approach includes a discussion on the performance of the individual predictors and their combination. © 2009 Taylor & Francis Group, LLC.

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