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A clustering algorithm is presented in order to group time series of maxima for pair of random variables recorded at different stations. The method is based on two kinds of dissimilarity measure. The first one involves the distance of the marginal distributions; the second one involves the distance between the respective copulas. In both cases, a Wasserstein metric is used. For the estimation, we rely on tools for extreme-value distributions and copulas.

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Regionalization Methods for Compound Extremes Based on the Wasserstein Distance

  • 会议论文

  • 发表日期：

    2025年

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  • Regina Castrovilli,
  • Fabrizio Durante,
  • Daniela Gallo,
  • Gianfausto Salvadori

摘要

A clustering algorithm is presented in order to group time series of maxima for pair of random variables recorded at different stations. The method is based on two kinds of dissimilarity measure. The first one involves the distance of the marginal distributions; the second one involves the distance between the respective copulas. In both cases, a Wasserstein metric is used. For the estimation, we rely on tools for extreme-value distributions and copulas.