Quantifying causal coupling strength: A lag-specific measure for multivariate time series related to transfer entropy

Jakob Runge, Jobst Heitzig, Norbert Marwan, and Jürgen Kurths
Phys. Rev. E 86, 061121 – Published 17 December 2012

Abstract

While it is an important problem to identify the existence of causal associations between two components of a multivariate time series, a topic addressed in Runge, Heitzig, Petoukhov, and Kurths [Phys. Rev. Lett. 108, 258701 (2012)], it is even more important to assess the strength of their association in a meaningful way. In the present article we focus on the problem of defining a meaningful coupling strength using information-theoretic measures and demonstrate the shortcomings of the well-known mutual information and transfer entropy. Instead, we propose a certain time-delayed conditional mutual information, the momentary information transfer (MIT), as a lag-specific measure of association that is general, causal, reflects a well interpretable notion of coupling strength, and is practically computable. Rooted in information theory, MIT is general in that it does not assume a certain model class underlying the process that generates the time series. As discussed in a previous paper [Runge, Heitzig, Petoukhov, and Kurths, Phys. Rev. Lett. 108, 258701 (2012)], the general framework of graphical models makes MIT causal in that it gives a nonzero value only to lagged components that are not independent conditional on the remaining process. Further, graphical models admit a low-dimensional formulation of conditions, which is important for a reliable estimation of conditional mutual information and, thus, makes MIT practically computable. MIT is based on the fundamental concept of source entropy, which we utilize to yield a notion of coupling strength that is, compared to mutual information and transfer entropy, well interpretable in that, for many cases, it solely depends on the interaction of the two components at a certain lag. In particular, MIT is, thus, in many cases able to exclude the misleading influence of autodependency within a process in an information-theoretic way. We formalize and prove this idea analytically and numerically for a general class of nonlinear stochastic processes and illustrate the potential of MIT on climatological data.

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  • Received 16 July 2012

DOI:https://doi.org/10.1103/PhysRevE.86.061121

©2012 American Physical Society

Authors & Affiliations

Jakob Runge1,2, Jobst Heitzig1, Norbert Marwan1, and Jürgen Kurths1,2,3

  • 1Potsdam Institute for Climate Impact Research (PIK), 14473 Potsdam, Germany
  • 2Department of Physics, Humboldt University, 12489 Berlin, Germany
  • 3Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen AB24 3UE, United Kingdom

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Issue

Vol. 86, Iss. 6 — December 2012

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