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Detecting Causality in Multivariate Time Series via Non-Uniform Embedding
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 no...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514578/ http://dx.doi.org/10.3390/e21121233 |
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author | Jia, Ziyu Lin, Youfang Jiao, Zehui Ma, Yan Wang, Jing |
author_facet | Jia, Ziyu Lin, Youfang Jiao, Zehui Ma, Yan Wang, Jing |
author_sort | Jia, Ziyu |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7514578 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75145782020-11-09 Detecting Causality in Multivariate Time Series via Non-Uniform Embedding Jia, Ziyu Lin, Youfang Jiao, Zehui Ma, Yan Wang, Jing Entropy (Basel) Article 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. MDPI 2019-12-16 /pmc/articles/PMC7514578/ http://dx.doi.org/10.3390/e21121233 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Jia, Ziyu Lin, Youfang Jiao, Zehui Ma, Yan Wang, Jing Detecting Causality in Multivariate Time Series via Non-Uniform Embedding |
title | Detecting Causality in Multivariate Time Series via Non-Uniform Embedding |
title_full | Detecting Causality in Multivariate Time Series via Non-Uniform Embedding |
title_fullStr | Detecting Causality in Multivariate Time Series via Non-Uniform Embedding |
title_full_unstemmed | Detecting Causality in Multivariate Time Series via Non-Uniform Embedding |
title_short | Detecting Causality in Multivariate Time Series via Non-Uniform Embedding |
title_sort | detecting causality in multivariate time series via non-uniform embedding |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514578/ http://dx.doi.org/10.3390/e21121233 |
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