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

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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)

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.

Suggested Citation

  • Alessia Benevento & Fabrizio Durante, 2023. "Wasserstein Dissimilarity for Copula-Based Clustering of Time Series with Spatial Information," Mathematics, MDPI, vol. 12(1), pages 1-15, December.
    • Handle: RePEc:gam:jmathe:v:12:y:2023:i:1:p:67-:d:1306786
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    References listed on IDEAS

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