A controlled transfer entropy approach to detect asymmetric interactions in heterogeneous systems

Abstract

Transfer entropy is emerging as the statistical approach of choice to support the inference of causal interactions in complex systems from time-series of their individual units. With reference to a simple dyadic system composed of two coupled units, the successful application of net transfer entropy-based inference relies on unidirectional coupling between the units and their homogeneous dynamics. What happens when the units are bidirectionally coupled and have different dynamics? Through analytical and numerical insights, we show that net transfer entropy may lead to erroneous inference of the dominant direction of influence that stems from its dependence on the units' individual dynamics. To control for these confounding effects, one should incorporate further knowledge about the units' time-histories through the recent framework offered by momentary information transfer. In this realm, we demonstrate the use of two measures: controlled and fully controlled transfer entropies, which consistently yield the correct direction of dominant coupling irrespective of the source's and target's individual dynamics. Through the study of two real-world examples, we identify critical limitations with respect to the use of net transfer entropy in the inference of causal mechanisms that warrant prudence by the community.

Validating a data-driven framework for vehicular traffic modeling

“…Specifically, transfer entropy (TE) and conditional transfer entropy (CTE) can quantify coupling between time-series variables, and therefore identify candidate variables for a model. These metrics have found successful applications in the study of various complex systems including human brain activity [24][25][26], animal collective behavior [27][28][29][30][31], climate modeling [32], policy-making [33][34][35][36][37] and financial markets [38,39]. However, its application within vehicular traffic systems has been relatively limited [40][41][42][43].…”