Grouping time series by pairwise measures of redundancy

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.

4 pages, 8 figures

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Cited by in corpus (3)

• Synergy and redundancy in the Granger causal analysis of dynamical networks
• A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data
• Causal information approach to partial conditioning in multivariate data sets