Time series clustering with a dissimilarity matrix based on tail dependence coefficients estimated by copula functions has been proposed in 2011 by De Luca and Zuccolotto, who used a two-step procedure allowing to resort to the k-means algorithm. The possibility to carry out hierarchical clustering directly on the dissimilarity matrix is still an open issue and the main concerns are relative to the meaning of the most common linkage methods in the context of tail dependence. In this paper, in a multivariate copula approach, we propose a linkage method based on the tail dependence coefficients between the clusters that are agglomerated at each iteration of the hierarchical clustering algorithms.

De Luca 和 Zuccolotto 于 2011 年提出了基于由 copula 函数估计的尾部相关系数的相异矩阵的时间序列聚类，他们使用了两步程序，允许诉诸k均值算法。直接在相异矩阵上进行层次聚类的可能性仍然是一个悬而未决的问题，主要关注点与尾依赖背景下最常见的链接方法的含义有关。在本文中，在多元 copula 方法中，我们提出了一种基于在分层聚类算法的每次迭代中聚集的聚类之间的尾部依赖系数的链接方法。