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 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Randy, S.J., Jain, A.K.: Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners. IEEE Trans. on Pattern Analysis and Machine Intelligence 13, 252–264 (1991)CrossRefGoogle Scholar
  2. 2.
    Mallat, S.: A Wavelet Tour of Signal Processing, 2nd edn. Academic Press, San Diego (1999)MATHGoogle Scholar
  3. 3.
    Chan, K.P., Fu, A.W., Clement, T.Y.: Harr Wavelets for Efficient Similarity Search of Time-Series: with and without Time Warping. IEEE Trans. on Knowledge and Data Engineering 15, 686–705 (2003)CrossRefGoogle Scholar
  4. 4.
    M\(\ddot{o}\)rchen, F.: Time Series Feature Extraction for Data Mining Using DWT and DFT. Technical Report No. 33, Dept. of Machmatics and Computer Science, University of Marburg, Germany (2003)Google Scholar
  5. 5.
    Zhang, H., Ho, T.B., Lin, M.S.: A Non-Parametric Wavelet Feature Extractor for Time Series Classification. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 595–603. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Zhang, H., Ho, T.B., Huang, W.: Blind Feature Extraction for Time-Series Classification Using Haar Wavelet Transform. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3497, pp. 605–610. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  7. 7.
    Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn., San Diego, CA (1990)Google Scholar
  8. 8.
    Specht, D.F.: Probabilistic Neural Networks. Neural Networks 11, 109–118 (1990)CrossRefGoogle Scholar
  9. 9.
    Keogh, E., Folias, T.: The UCR Time Series Data Mining Archive (2002),
  10. 10.
    Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K., Stanley, H.E.: PhysioBank, PhysioToolkit, and PhysioNet: Components of A New Research Resource for Complex Physiologic Signals. Circulation 101, e215–e220 (2000), Google Scholar

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

Personalised recommendations