Local prediction of non-linear time series using support vector regression

Authors:
K. W. Lau;Q. H. Wu
Affiliations:
Department of Electrical Engineering and Electronics, The University of Liverpool, Liverpool L69 3GJ, UK;Department of Electrical Engineering and Electronics, The University of Liverpool, Liverpool L69 3GJ, UK
Venue:
Pattern Recognition
Year:
2008

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Abstract

Prediction on complex time series has received much attention during the last decade. This paper reviews least square and radial basis function based predictors and proposes a support vector regression (SVR) based local predictor to improve phase space prediction of chaotic time series by combining the strength of SVR and the reconstruction properties of chaotic dynamics. The proposed method is applied to Henon map and Lorenz flow with and without additive noise, and also to Sunspots time series. The method provides a relatively better long term prediction performance in comparison with the others.