CLASSIFICATION OF TIME SERIES VIA WAVELET SUBBAND ANALYSIS USING SUPPORT VECTOR MACHINE CLASSIFIER

Abstract

An improved feature extraction method has been developed for classification and identification of time series, in case of the number of the experiments are considerably less than that of the samples in time series. The method based on the subband analysis of the wavelet transformation of the time signals, provides lower dimension feature vectors as well as much more robust kernel-based classifier than the traditional wavelet-based feature extraction method does. The application of this technique is illustrated by the classification of cerebral blood flow oscillation using support vector classifier with Gaussian kernel. The computations were carried out with Mathematica 5.1 and its Wavelet Application.