Direct coupling information measure from non-uniform embedding

TL;DR

This paper introduces PMIME, a new information measure for detecting direct, directional coupling in multivariate time series, which outperforms existing methods and is robust without relying on significance tests.

Contribution

The paper proposes Partial MIME (PMIME), an extension of MIME, for better detection of direct coupling in multivariate time series, with improved accuracy and computational efficiency.

Findings

PMIME accurately detects direct coupling in multivariate data.

PMIME outperforms linear Granger causality and transfer entropy.

PMIME is robust to the number of observed variables and does not require significance testing.

Abstract

A measure to estimate the direct and directional coupling in multivariate time series is proposed. The measure is an extension of a recently published measure of conditional Mutual Information from Mixed Embedding (MIME) for bivariate time series. In the proposed measure of Partial MIME (PMIME), the embedding is on all observed variables, and it is optimized in explaining the response variable. It is shown that PMIME detects correctly direct coupling, and outperforms the (linear) conditional Granger causality and the partial transfer entropy. We demonstrate that PMIME does not rely on significance test and embedding parameters, and the number of observed variables has no effect on its statistical accuracy, it may only slow the computations. The importance of these points is shown in simulations and in an application to epileptic multi-channel scalp EEG.