Open AccessPrediction of chaotic time-series based on online wavelet support vector regression
Author(s) -
Zhenhua Yu,
Yuanli Cai
Publication year - 2006
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.55.1659
Subject(s) - series (stratigraphy) , computer science , chaotic , wavelet , time series , support vector machine , regression , artificial intelligence , statistical physics , data mining , pattern recognition (psychology) , machine learning , statistics , mathematics , physics , geology , paleontology
Support vector regression (SVR) is an effective method for the predication of chaotic time-series, which is a fundamental topic of nonlinear dynamics. Through analyzing the possible variation of support vector sets after new samples are inserted to the training set, a novel SVR algorithm is proposed; thus an online learning algorithm is set up. In connection with the specific characteristics of chaotic signals, a wavelet kernel satisfying wavelet frames is also presented. The wavelet kernel can approximate arbitrary functions, and is especially suitable for local processing; hence the generalization ability of SVR is improved. To illustrate the good performance of the online wavelet SVR, a benchmark problem, i.e. the online prediction of chaotic Mackey-Glass time-series, is considered. The simulation results indicate that the online wavelet SVR algorithm outperforms the existing algorithms in higher efficiency of learning as well as better accuracy of prediction.