International Journal of Computational Intelligence Systems

Volume 2, Issue 2, June 2009, Pages 147 - 157

Radial Basis Function Nets for Time Series Prediction

Authors
Abdelhamid Bouchachia
Corresponding Author
Abdelhamid Bouchachia
Available Online 16 June 2009.
DOI
https://doi.org/10.2991/ijcis.2009.2.2.6How to use a DOI?
Keywords
NARX Architecture, Radial basis function networks, Ensemble predictors, Multi-step prediction
Abstract
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 MS- NARX-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.
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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
2 - 2
Pages
147 - 157
Publication Date
2009/06
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
https://doi.org/10.2991/ijcis.2009.2.2.6How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - JOUR
AU  - Abdelhamid Bouchachia
PY  - 2009
DA  - 2009/06
TI  - Radial Basis Function Nets for Time Series Prediction
JO  - International Journal of Computational Intelligence Systems
SP  - 147
EP  - 157
VL  - 2
IS  - 2
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2009.2.2.6
DO  - https://doi.org/10.2991/ijcis.2009.2.2.6
ID  - Bouchachia2009
ER  -