Hierarchical Clustering of Time Series with Wasserstein Distance
摘要
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.