Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Jia, Ziyu, Lin, Youfang, Jiao, Zehui, Ma, Yan, Wang, Jing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514578/
http://dx.doi.org/10.3390/e21121233
_version_ 1783586620307406848
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
work_keys_str_mv AT jiaziyu detectingcausalityinmultivariatetimeseriesvianonuniformembedding
AT linyoufang detectingcausalityinmultivariatetimeseriesvianonuniformembedding
AT jiaozehui detectingcausalityinmultivariatetimeseriesvianonuniformembedding
AT mayan detectingcausalityinmultivariatetimeseriesvianonuniformembedding
AT wangjing detectingcausalityinmultivariatetimeseriesvianonuniformembedding