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
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;
Conference_Titel :
Intelligent Computing and Integrated Systems (ICISS), 2010 International Conference on
Conference_Location :
Guilin
Print_ISBN :
978-1-4244-6834-8
DOI :
10.1109/ICISS.2010.5656104