Synergy and Redundancy Dominated Effects in Time Series via Transfer Entropy Decompositions

TL;DR

This paper introduces a novel transfer entropy decomposition method to analyze how different parts of a time series' past influence interactions, revealing synergy or redundancy effects, with practical application to brain data.

Contribution

It proposes a new transfer entropy decomposition that distinguishes synergy and redundancy effects in time series, supported by theoretical proof and real-world brain data analysis.

Findings

Early past shows synergy effects

Late past exhibits redundancy effects

Method is practical and applicable to brain data

Abstract

We present a new decomposition of transfer entropy to characterize the degree of synergy- and redundancy-dominated influence a time series has upon the interaction between other time series. We prove the existence of a class of time series, where the early past of the conditioning time series yields a synergistic effect upon the interaction, whereas the late past has a redundancy-dominated effect. In general, different parts of the past can have different effects. Our information theoretic quantities are easy to compute in practice, and we demonstrate their usage on real-world brain data.