In this work, a new approach to cluster large sets of time series is presented. The proposed methodology takes into account the dependency among the time series to obtain a fuzzy partition of the set of observations. A two-step procedure to accomplish this is presented. First, the cophenetic distances, based on a time series linear cross-dependency measure, are obtained. Second, these distances are used as an input of a non-Euclidean fuzzy relational clustering algorithm. As a result, we obtain a robust fuzzy procedure capable of detecting groups of time series with different types of cross-dependency. We illustrate the usefulness of the stated methodology through some Monte Carlo experiments and a real data example. Our results show that the methodology proposed in this work substantially improves the hard partitioning clustering alternative. (C) 2021 The Authors. Published by Elsevier Inc.

Cophenetic-based fuzzy clustering of time series by linear dependency / Alonso, Andrés M.; D'Urso, Pierpaolo; Gamboa, Carolina; Guerrero, Vanesa. - In: INTERNATIONAL JOURNAL OF APPROXIMATE REASONING. - ISSN 0888-613X. - 137:(2021), pp. 114-136. [10.1016/j.ijar.2021.07.006]

Cophenetic-based fuzzy clustering of time series by linear dependency

Pierpaolo D'Urso;
2021

Abstract

In this work, a new approach to cluster large sets of time series is presented. The proposed methodology takes into account the dependency among the time series to obtain a fuzzy partition of the set of observations. A two-step procedure to accomplish this is presented. First, the cophenetic distances, based on a time series linear cross-dependency measure, are obtained. Second, these distances are used as an input of a non-Euclidean fuzzy relational clustering algorithm. As a result, we obtain a robust fuzzy procedure capable of detecting groups of time series with different types of cross-dependency. We illustrate the usefulness of the stated methodology through some Monte Carlo experiments and a real data example. Our results show that the methodology proposed in this work substantially improves the hard partitioning clustering alternative. (C) 2021 The Authors. Published by Elsevier Inc.
2021
Fuzzy clustering; Time series; Hierarchical clustering; Cophenetic distances
01 Pubblicazione su rivista::01a Articolo in rivista
Cophenetic-based fuzzy clustering of time series by linear dependency / Alonso, Andrés M.; D'Urso, Pierpaolo; Gamboa, Carolina; Guerrero, Vanesa. - In: INTERNATIONAL JOURNAL OF APPROXIMATE REASONING. - ISSN 0888-613X. - 137:(2021), pp. 114-136. [10.1016/j.ijar.2021.07.006]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1661231
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