This paper proposes a fuzzy C-medoids-based clustering method with entropy regularization to solve the issue of grouping complex data as interval-valued time series. The dual nature of the data, that are both time-varying and interval-valued, needs to be considered and embedded into clustering techniques. In this work, a new dissimilarity measure, based on Dynamic Time Warping, is proposed. The performance of the new clustering procedure is evaluated through a simulation study and an application to financial time series.
Entropy-based fuzzy clustering of interval-valued time series / Vitale, Vincenzina; D'Urso, Pierpaolo; De Giovanni, Livia; Mattera, R.. - In: ADVANCES IN DATA ANALYSIS AND CLASSIFICATION. - ISSN 1862-5355. - (In corso di stampa), pp. 1-27. [10.1007/s11634-024-00586-6]
Entropy-based fuzzy clustering of interval-valued time series
Vitale V.
;D'Urso P.;De Giovanni L.;
In corso di stampa
Abstract
This paper proposes a fuzzy C-medoids-based clustering method with entropy regularization to solve the issue of grouping complex data as interval-valued time series. The dual nature of the data, that are both time-varying and interval-valued, needs to be considered and embedded into clustering techniques. In this work, a new dissimilarity measure, based on Dynamic Time Warping, is proposed. The performance of the new clustering procedure is evaluated through a simulation study and an application to financial time series.
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