Volume 2, Issue 2, June 2009, Pages 147 - 157
Radial Basis Function Nets for Time Series Prediction
Received 30 September 2008, Revised 19 May 2009, Available Online 1 June 2009.
- https://doi.org/10.2991/ijcis.2009.2.2.6How to use a DOI?
- NARX Architecture, Radial basis function networks, Ensemble predictors, Multi-step prediction
- 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.
- 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 -