Chaotic time series forecasting using online least squares support vector machine regression

Abstract

A chaotic time series forecasting method based on online least squares support vector machine (LS-SVM) regression is proposed. The difference between the online LS-SVM and offline support vector machine (SVM) is that the online LS-SVM is still effective for the chaotic system with a variation of the system parameter. Four chaotic time series, namely, Chen's system, Rssler system, Hénon map an d chaotic electroencephalogram (EEG) signal, are used to evaluate the performanc e. The results verify the ability of the method in chaotic time series predictio n.