Detecting causality in multivariate time series via non-uniform embedding

Abstract

Causal analysis based on non-uniform embedding schemes is an important way to detect the underlying interactions between dynamic systems. However, there are still some obstacles to estimate high-dimensional conditional mutual information and form optimal mixed embedding vector in traditional non-uniform embedding schemes. In this study, we present a new non-uniform embedding method framed in information theory to detect causality for multivariate time series, named LM-PMIME, which integrates the low-dimensional approximation of conditional mutual information and the mixed search strategy for the construction of the mixed embedding vector. We apply the proposed method to simulations of linear stochastic, nonlinear stochastic, and chaotic systems, demonstrating its superiority over partial conditional mutual information from mixed embedding (PMIME) method. Moreover, the proposed method works well for multivariate time series with weak coupling strengths, especially for chaotic systems. In the actual application, we show its applicability to epilepsy multichannel electrocorticographic recordings.

References in corpus (9)

• Measuring Information Transfer
• Estimating Mutual Information
• Granger causality and transfer entropy are equivalent for Gaussian variables
• Kernel method for nonlinear Granger causality
• Causal Network Inference by Optimal Causation Entropy
• Non-uniform state space reconstruction and coupling detection
• Direct coupling information measure from non-uniform embedding
• Multiscale Information Decomposition: Exact Computation for Multivariate Gaussian Processes
• Optimal model-free prediction from multivariate time series

Cited by in corpus (1)

• Estimating Conditional Transfer Entropy in Time Series using Mutual Information and Non-linear Prediction