Hierarchical Clustering of Time Series with Wasserstein Distance

Alessia Benevento (), Fabrizio Durante (), Daniela Gallo () and Aurora Gatto ()
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Alessia Benevento: Università del Salento
Fabrizio Durante: Università del Salento
Daniela Gallo: Università del Salento
Aurora Gatto: Free University of Bozen-Bolzano

A chapter in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2024, pp 49-54 from Springer

Abstract: Abstract Two methods are presented in order to create a dissimilarity measure for random variables. These methods exploit some theoretical and computational advantages of the Wasserstein distance. The dissimilarity measures are hence applied to develop a hierarchical clustering procedure for time series, which are especially helpful in risk analysis.

Keywords: Clustering; Copula; Risk; Wasserstein distance (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-64273-9_9

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DOI: 10.1007/978-3-031-64273-9_9

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