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A functional data based method for time series classification

  • Andrés M. Alonso

    ()

  • David Casado

    ()

  • Sara López Pintado

    ()

  • Juan Romo

    ()

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    We propose using the integrated periodogram to classify time series. The method assigns a new element to the group minimizing the distance from the integrated periodogram of the element to the group mean of integrated periodograms. Local computation of these periodograms allows the application of the approach to nonstationary time series. Since the integrated periodograms are functional data, we apply depth-based techniques to make the classification robust. The method provides small error rates with both simulated and real data, and shows good computational behaviour.

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    File URL: http://e-archivo.uc3m.es/bitstream/10016/3381/5/ws087427.pdf
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    Paper provided by Universidad Carlos III, Departamento de Estadística y Econometría in its series Statistics and Econometrics Working Papers with number ws087427.

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    Date of creation: Jan 2009
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    Handle: RePEc:cte:wsrepe:ws087427
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    1. James G.M. & Sugar C.A., 2003. "Clustering for Sparsely Sampled Functional Data," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 397-408, January.
    2. Gareth M. James & Trevor J. Hastie, 2001. "Functional linear discriminant analysis for irregularly sampled curves," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(3), pages 533-550.
    3. Maharaj, Elizabeth A. & Alonso, Andres M., 2007. "Discrimination of locally stationary time series using wavelets," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 879-895, October.
    4. Sakiyama, Kenji & Taniguchi, Masanobu, 2004. "Discriminant analysis for locally stationary processes," Journal of Multivariate Analysis, Elsevier, vol. 90(2), pages 282-300, August.
    5. Chandler, Gabriel & Polonik, Wolfgang, 2006. "Discrimination of Locally Stationary Time Series Based on the Excess Mass Functional," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 240-253, March.
    6. Shumway, Robert H., 2003. "Time-frequency clustering and discriminant analysis," Statistics & Probability Letters, Elsevier, vol. 63(3), pages 307-314, July.
    7. Ferraty, F. & Vieu, P., 2003. "Curves discrimination: a nonparametric functional approach," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 161-173, October.
    8. Ombao H. C & Raz J. A & von Sachs R. & Malow B. A, 2001. "Automatic Statistical Analysis of Bivariate Nonstationary Time Series," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 543-560, June.
    9. Hsiao-Yun Huang & Hernando Ombao & David S. Stoffer, 2004. "Discrimination and Classification of Nonstationary Time Series Using the SLEX Model," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 763-774, January.
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