Transfer Entropy on Rank Vectors

TL;DR

This paper introduces Transfer Entropy on Rank Vectors (TERV), a modified symbolic transfer entropy measure that improves detection of information flow, especially under noisy conditions, by considering multiple steps ahead and correcting rank calculations.

Contribution

The paper proposes TERV, a novel modification of STE, with a correction for rank calculation and multi-step ahead analysis, enhancing information flow detection accuracy.

Findings

TERV outperforms STE in noisy environments.

Using multiple steps ahead improves measure accuracy.

TE remains more consistent overall.

Abstract

Transfer entropy (TE) is a popular measure of information flow found to perform consistently well in different settings. Symbolic transfer entropy (STE) is defined similarly to TE but on the ranks of the components of the reconstructed vectors rather than the reconstructed vectors themselves. First, we correct STE by forming the ranks for the future samples of the response system with regard to the current reconstructed vector. We give the grounds for this modified version of STE, which we call Transfer Entropy on Rank Vectors (TERV). Then we propose to use more than one step ahead in the formation of the future of the response in order to capture the information flow from the driving system over a longer time horizon. To assess the performance of STE, TE and TERV in detecting correctly the information flow we use receiver operating characteristic (ROC) curves formed by the measure values in the two coupling directions computed on a number of realizations of known weakly coupled systems. We also consider different settings of state space reconstruction, time series length and observational noise. The results show that TERV indeed improves STE and in some cases performs better than TE, particularly in the presence of noise, but overall TE gives more consistent results. The use of multiple steps ahead improves the accuracy of TE and TERV.