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Pattern Recognition of Time Series using Wavelets

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Abstract

In this paper a pattern recognition procedure for time series using wavelets is developed. This is done by means of a randomization test based on the ratio of the sum of squared wavelet coefficients of pairs of time series at different scales. A simulation study using pairs of stationary and non-stationary time series and using the Haar and Daubechies wavelets reveals that the test performs fairly well at scales where there are a sufficient number of wavelet coefficients. The test is applied to a set of financial time series.

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References

  • Basawa, I.V., Billard, L. & Srinivasan, R. (1984). Large-sample tests of homogeneity for time series models. Biometrika 71 203–206.

    Article  MathSciNet  MATH  Google Scholar 

  • Diggle, P.J. and Fisher, N.I. (1991). Nonparametric comparison of cumulative periodograms. Appl. Statist. 40 423–434.

    Article  MathSciNet  MATH  Google Scholar 

  • Guo, J. H. (1999). A non-parametric test for the parallelism of two first-order autoregressive processes. Aust. N. Z. J. Stat. 41 59–65.

    Article  MathSciNet  MATH  Google Scholar 

  • Maharaj, E.A. (2000). Clusters of time series. J. Classification 17 297–314.

    Article  MathSciNet  MATH  Google Scholar 

  • Maharaj, E.A. (2002). Comparison of non-stationary time series in the frequency Domain. To appear in Comput. Statist. Data Anal.

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  • Percival, D.B. & Walden, A.T. (1999). Wavelet methods for time series analysis. Cambridge: Cambridge University Press.

    Google Scholar 

  • Timmer, J., Lauk, M., Vach, W. & Lucking, C.H. (1999). A test for the difference between spectral peak frequencies. Comput. Statist. Data Anal. 30, 45–55.

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Author information

Authors and Affiliations

  1. Department of Econometrics and Business Statistics, Monash University, Australia

    Elizabeth Ann Maharaj

Authors
  1. Elizabeth Ann Maharaj
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Editor information

Editors and Affiliations

  1. CASE — Centre of Applied Statistics and Economics, Humboldt-Universität zu Berlin, Spandauer Straße 1, 10178, Berlin, Germany

    Wolfgang Härdle

  2. Wirtschaftswissenschaftliche Fakultät, Institut für Statistik und Ökonometrie, Humboldt-Universität zu Berlin, Spandauer Straße 1, 10178, Berlin, Germany

    Bernd Rönz

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© 2002 Springer-Verlag Berlin Heidelberg

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Maharaj, E.A. (2002). Pattern Recognition of Time Series using Wavelets. In: Härdle, W., Rönz, B. (eds) Compstat. Physica, Heidelberg. https://doi.org/10.1007/978-3-642-57489-4_76

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  • DOI: https://doi.org/10.1007/978-3-642-57489-4_76

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-1517-7

  • Online ISBN: 978-3-642-57489-4

  • eBook Packages: Springer Book Archive

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