  This paper presents a new methodology for clustering multivariate time series
leveraging optimal transport between copulas. Copulas are used to encode both
(i) intra-dependence of a multivariate time series, and (ii) inter-dependence
between two time series. Then, optimal copula transport allows us to define two
distances between multivariate time series: (i) one for measuring
intra-dependence dissimilarity, (ii) another one for measuring inter-dependence
dissimilarity based on a new multivariate dependence coefficient which is
robust to noise, deterministic, and which can target specified dependencies.
