Open AccessChaotic time series forecasting using online least squares support vector machine regression
Author(s) -
Meiying Ye,
Xiaodong Wang,
Haoran Zhang
Publication year - 2005
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.54.2568
Subject(s) - support vector machine , chaotic , computer science , series (stratigraphy) , time series , least squares function approximation , artificial intelligence , machine learning , least squares support vector machine , algorithm , regression , pattern recognition (psychology) , statistics , mathematics , paleontology , estimator , biology
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, Rssler 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.