Fuzzy clustering of time series based on weighted conditional higher moments

Roy Cerqueti (), Pierpaolo D’Urso (), Livia Giovanni (), Raffaele Mattera () and Vincenzina Vitale ()
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Roy Cerqueti: Sapienza University of Rome
Pierpaolo D’Urso: Sapienza University of Rome
Livia Giovanni: LUISS Guido Carli
Raffaele Mattera: Sapienza University of Rome
Vincenzina Vitale: Sapienza University of Rome

Computational Statistics, 2024, vol. 39, issue 6, No 9, 3114 pages

Abstract: 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.

Keywords: Dynamic conditional score; Unsupervised learning; Robust clustering; Fuzzy clustering; Conditional moments; Exponential dissimilarity; Financial time series (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00180-023-01425-6

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