Grouping time series by pairwise measures of redundancy

TL;DR

This paper introduces a new method for clustering redundant time series based on pairwise redundancy measures, applied to brain and gene expression data, assuming few characteristic modes describe system dynamics.

Contribution

It presents a novel approach that uses pairwise redundancy to identify correlated degrees of freedom in time series data, applicable to neuroscience and genomics.

Findings

Successfully applied to fMRI data revealing brain region interactions.

Effectively used on gene expression profiles from HeLa cells.

Demonstrates the method's ability to identify meaningful clusters.

Abstract

A novel approach is proposed to group redundant time series in the frame of causality. It assumes that (i) the dynamics of the system can be described using just a small number of characteristic modes, and that (ii) a pairwise measure of redundancy is sufficient to elicit the presence of correlated degrees of freedom. We show the application of the proposed approach on fMRI data from a resting human brain and gene expression profiles from HeLa cell culture.