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Robust Functional Supervised Classification for Time Series

Author

Listed:
  • Andrés Alonso
  • David Casado
  • Sara López-Pintado
  • Juan Romo

Abstract

We propose using the integrated periodogram to classify time series. The method assigns a new time series to the group that minimizes the distance between the series integrated periodogram and the group mean of integrated periodograms. Local computation of these periodograms allows the application of this approach to nonstationary time series. Since the integrated periodograms are curves, we apply functional data depth-based techniques to make the classification robust, which is a clear advantage over other competitive procedures. The method provides small error rates for both simulated and real data. It improves existing approaches and presents good computational behavior. Copyright Classification Society of North America 2014

Suggested Citation

  • Andrés Alonso & David Casado & Sara López-Pintado & Juan Romo, 2014. "Robust Functional Supervised Classification for Time Series," Journal of Classification, Springer;The Classification Society, vol. 31(3), pages 325-350, October.
    • Handle: RePEc:spr:jclass:v:31:y:2014:i:3:p:325-350
    DOI: 10.1007/s00357-014-9163-x
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    References listed on IDEAS

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    1. López-Pintado, Sara & Romo, Juan, 2009. "On the Concept of Depth for Functional Data," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 718-734.
    2. 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.
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    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. 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.
    6. C. Abraham & P. A. Cornillon & E. Matzner‐Løber & N. Molinari, 2003. "Unsupervised Curve Clustering using B‐Splines," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 30(3), pages 581-595, September.
    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. 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.
    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.
    10. 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.
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    Cited by:

    1. Guochang Wang & Xinyuan Song, 2018. "Functional Sufficient Dimension Reduction for Functional Data Classification," Journal of Classification, Springer;The Classification Society, vol. 35(2), pages 250-272, July.
    2. Ángel López-Oriona & José A. Vilar, 2021. "F4: An All-Purpose Tool for Multivariate Time Series Classification," Mathematics, MDPI, vol. 9(23), pages 1-26, November.
    3. Douglas L. Steinley, 2016. "Editorial," Journal of Classification, Springer;The Classification Society, vol. 33(3), pages 327-330, October.

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