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Fuzzy clustering of time series based on weighted conditional higher moments

Author

Listed:
  • Roy Cerqueti

    (Sapienza University of Rome
    University of Angers)

  • Pierpaolo D’Urso

    (Sapienza University of Rome)

  • Livia Giovanni

    (LUISS Guido Carli)

  • Raffaele Mattera

    (Sapienza University of Rome)

  • Vincenzina Vitale

    (Sapienza University of Rome)

Abstract

This paper proposes a new approach to fuzzy clustering of time series based on the dissimilarity among conditional higher moments. A system of weights accounts for the relevance of each conditional moment in defining the clusters. Robustness against outliers is also considered by extending the above clustering method using a suitable exponential transformation of the distance measure defined on the conditional higher moments. To show the usefulness of the proposed approach, we provide a study with simulated data and an empirical application to the time series of stocks included in the FTSEMIB 30 Index.

Suggested Citation

  • Roy Cerqueti & Pierpaolo D’Urso & Livia Giovanni & Raffaele Mattera & Vincenzina Vitale, 2024. "Fuzzy clustering of time series based on weighted conditional higher moments," Computational Statistics, Springer, vol. 39(6), pages 3091-3114, September.
    • Handle: RePEc:spr:compst:v:39:y:2024:i:6:d:10.1007_s00180-023-01425-6
    DOI: 10.1007/s00180-023-01425-6
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    References listed on IDEAS

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