Optimal Copula Transport for Clustering Multivariate Time Series
Machine Learning
2016-01-12 v2 Machine Learning
Abstract
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.
Keywords
Cite
@article{arxiv.1509.08144,
title = {Optimal Copula Transport for Clustering Multivariate Time Series},
author = {Gautier Marti and Frank Nielsen and Philippe Donnat},
journal= {arXiv preprint arXiv:1509.08144},
year = {2016}
}
Comments
Accepted at ICASSP 2016