학술논문
Optimal transport vs. Fisher-Rao distance between copulas for clustering multivariate time series
Document Type
Conference
Author
Source
2016 IEEE Statistical Signal Processing Workshop (SSP) Statistical Signal Processing Workshop (SSP), 2016 IEEE. :1-5 Jun, 2016
Subject
Language
Abstract
We present a methodology for clustering N objects which are described by multivariate time series, i.e. several sequences of real-valued random variables. This clustering methodology leverages copulas which are distributions encoding the dependence structure between several random variables. To take fully into account the dependence information while clustering, we need a distance between copulas. In this work, we compare renowned distances between distributions: the Fisher-Rao geodesic distance, related divergences and optimal transport, and discuss their advantages and disadvantages. Applications of such methodology can be found in the clustering of financial assets. A tutorial, experiments and implementation for reproducible research can be found at www.datagrapple.com/Tech.