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Open AccessArticle

Detecting Causality in Multivariate Time Series via Non-Uniform Embedding

1
School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
2
Division of Interdisciplinary Medicine and Biotechnology, Department of Medicine, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, MA 02215, USA
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(12), 1233; https://doi.org/10.3390/e21121233
Received: 11 November 2019 / Revised: 29 November 2019 / Accepted: 13 December 2019 / Published: 16 December 2019
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 estimating high-dimensional conditional mutual information and forming 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. View Full-Text
Keywords: causal analysis; non-uniform embedding; multivariate time series; conditional mutual information causal analysis; non-uniform embedding; multivariate time series; conditional mutual information
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Jia, Z.; Lin, Y.; Jiao, Z.; Ma, Y.; Wang, J. Detecting Causality in Multivariate Time Series via Non-Uniform Embedding. Entropy 2019, 21, 1233.

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