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 TimeWarping, 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, Raffaele. - In: ADVANCES IN DATA ANALYSIS AND CLASSIFICATION. - ISSN 1862-5347. - (2024). [10.1007/s11634-024-00586-6]

Entropy-based fuzzy clustering of interval-valued time series

Vitale, Vincenzina;D’Urso, Pierpaolo;Mattera, Raffaele
2024

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 TimeWarping, is proposed. The performance of the new clustering procedure is evaluated through a simulation study and an application to financial time series.
2024
Interval-valued time series; Fuzzy clustering; Dynamic time warping; FTSE-MIB index
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Entropy-based fuzzy clustering of interval-valued time series / Vitale, Vincenzina; D’Urso, Pierpaolo; De Giovanni, Livia; Mattera, Raffaele. - In: ADVANCES IN DATA ANALYSIS AND CLASSIFICATION. - ISSN 1862-5347. - (2024). [10.1007/s11634-024-00586-6]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1708855
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