Next Article in Journal
Mathematics and the Brain: A Category Theoretical Approach to Go Beyond the Neural Correlates of Consciousness
Next Article in Special Issue
Measuring Information Coupling between the Solar Wind and the Magnetosphere–Ionosphere System
Previous Article in Journal
Progress in Carnot and Chambadal Modeling of Thermomechanical Engine by Considering Entropy Production and Heat Transfer Entropy
Previous Article in Special Issue
Residual Predictive Information Flow in the Tight Coupling Limit: Analytic Insights from a Minimalistic Model
Open AccessArticle

Detecting Causality in Multivariate Time Series via Non-Uniform Embedding

School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
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;
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
Show Figures

Figure 1

MDPI and ACS Style

Jia, Z.; Lin, Y.; Jiao, Z.; Ma, Y.; Wang, J. Detecting Causality in Multivariate Time Series via Non-Uniform Embedding. Entropy 2019, 21, 1233.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop