Feature Extraction for Time Series Classification Using Discriminating Wavelet Coefficients

  • Hui Zhang
  • Tu Bao Ho
  • Mao-Song Lin
  • Xuefeng Liang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)


Many feature extraction algorithms have been proposed for time series classification. However, most of the proposed algorithms in time series data mining community belong to the unsupervised approach, without considering the class separability capability of features that is important for classification. In this paper we propose a supervised feature extraction approach by selecting discriminating wavelet coefficients to improve the time series classification accuracy. After wavelet transformation, few wavelet coefficients with higher class separability capability are selected as features. We apply three feature evaluation criteria, i.e., Fisher’s discriminant ratio, divergence, and Bhattacharyya distance. Experiments performed on several benchmark time series datasets demonstrate the effectiveness of the proposed approach.


Time Series Feature Extraction Original Time Series Feature Extraction Algorithm Bhattacharyya Distance 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hui Zhang
    • 1
    • 2
  • Tu Bao Ho
    • 1
  • Mao-Song Lin
    • 2
  • Xuefeng Liang
    • 1
  1. 1.School of Knowledge ScienceJapan Advanced Institute of Science and TechnologyNomi, IshikawaJapan
  2. 2.School of Computer ScienceSouthwest University of Science and TechnologyMianyangChina

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