DocumentCode :
3407778
Title :
Period analysis based on SVM and wavelet variance for time series
Author :
Cai, Ruhua ; Fan, Qibin
Author_Institution :
Sch. of Math. & Compute Sci., Guilin Univ. of Electron. Technol., Guilin, China
fYear :
2010
fDate :
22-24 Oct. 2010
Firstpage :
404
Lastpage :
409
Abstract :
In the time series period analysis, the period obtained by the maximal wavelet variance has serious errors. In this paper, we present a method of LS-SVM to approximate the wavelet variance or power spectrum at different scales, and then obtain the period of sequence by estimating the maximum value. The experiment indicates that LS-SVM method can approximate wavelet variance effectively, and can estimate the period of the time series accurately. It is an effective method for time series period analysis and power spectral analysis.
Keywords :
learning (artificial intelligence); support vector machines; time series; wavelet transforms; LS-SVM method; SVM variance; period analysis; power spectral analysis; power spectrum; time series; wavelet variance; Estimation; LS-SVM; MODWT; Time Series Period; Wavelet Variance;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Computing and Integrated Systems (ICISS), 2010 International Conference on
Conference_Location :
Guilin
Print_ISBN :
978-1-4244-6834-8
Type :
conf
DOI :
10.1109/ICISS.2010.5656104
Filename :
5656104
Link To Document :
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