Wasserstein Dissimilarity for Copula-Based Clustering of Time Series with Spatial Information

Alessia Benevento and Fabrizio Durante ()
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Alessia Benevento: Dipartimento di Scienze dell’Economia, Università del Salento, 73100 Lecce, Italy
Fabrizio Durante: Dipartimento di Scienze dell’Economia, Università del Salento, 73100 Lecce, Italy

Mathematics, 2023, vol. 12, issue 1, 1-15

Abstract: The clustering of time series with geo-referenced data requires a suitable dissimilarity matrix interpreting the comovements of the time series and taking into account the spatial constraints. In this paper, we propose a new way to compute the dissimilarity matrix, merging both types of information, which leverages on the Wasserstein distance. We then make a quasi-Gaussian assumption that yields more convenient formulas in terms of the joint correlation matrix. The method is illustrated in a case study involving climatological data.

Keywords: clustering; copula; dissimilarity matrix; optimal transport; time series; Wasserstein distance (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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