Large-scale nonlinear Granger causality for inferring directed dependence from short multivariate time-series data

Abstract A key challenge to gaining insight into complex systems is inferring nonlinear causal directional relations from observational time-series data. Specifically, estimating causal relationships between interacting components in large systems with only short recordings over few temporal observa...

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Autores principales:	Axel Wismüller, Adora M. Dsouza, M. Ali Vosoughi, Anas Abidin
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f25323c5047c44b7a97ce17913dbbeed
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spelling	oai:doaj.org-article:f25323c5047c44b7a97ce17913dbbeed2021-12-02T14:25:55ZLarge-scale nonlinear Granger causality for inferring directed dependence from short multivariate time-series data10.1038/s41598-021-87316-62045-2322https://doaj.org/article/f25323c5047c44b7a97ce17913dbbeed2021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87316-6https://doaj.org/toc/2045-2322Abstract A key challenge to gaining insight into complex systems is inferring nonlinear causal directional relations from observational time-series data. Specifically, estimating causal relationships between interacting components in large systems with only short recordings over few temporal observations remains an important, yet unresolved problem. Here, we introduce large-scale nonlinear Granger causality (lsNGC) which facilitates conditional Granger causality between two multivariate time series conditioned on a large number of confounding time series with a small number of observations. By modeling interactions with nonlinear state-space transformations from limited observational data, lsNGC identifies casual relations with no explicit a priori assumptions on functional interdependence between component time series in a computationally efficient manner. Additionally, our method provides a mathematical formulation revealing statistical significance of inferred causal relations. We extensively study the ability of lsNGC in inferring directed relations from two-node to thirty-four node chaotic time-series systems. Our results suggest that lsNGC captures meaningful interactions from limited observational data, where it performs favorably when compared to traditionally used methods. Finally, we demonstrate the applicability of lsNGC to estimating causality in large, real-world systems by inferring directional nonlinear, causal relationships among a large number of relatively short time series acquired from functional Magnetic Resonance Imaging (fMRI) data of the human brain.Axel WismüllerAdora M. DsouzaM. Ali VosoughiAnas AbidinNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Axel Wismüller Adora M. Dsouza M. Ali Vosoughi Anas Abidin Large-scale nonlinear Granger causality for inferring directed dependence from short multivariate time-series data
description	Abstract A key challenge to gaining insight into complex systems is inferring nonlinear causal directional relations from observational time-series data. Specifically, estimating causal relationships between interacting components in large systems with only short recordings over few temporal observations remains an important, yet unresolved problem. Here, we introduce large-scale nonlinear Granger causality (lsNGC) which facilitates conditional Granger causality between two multivariate time series conditioned on a large number of confounding time series with a small number of observations. By modeling interactions with nonlinear state-space transformations from limited observational data, lsNGC identifies casual relations with no explicit a priori assumptions on functional interdependence between component time series in a computationally efficient manner. Additionally, our method provides a mathematical formulation revealing statistical significance of inferred causal relations. We extensively study the ability of lsNGC in inferring directed relations from two-node to thirty-four node chaotic time-series systems. Our results suggest that lsNGC captures meaningful interactions from limited observational data, where it performs favorably when compared to traditionally used methods. Finally, we demonstrate the applicability of lsNGC to estimating causality in large, real-world systems by inferring directional nonlinear, causal relationships among a large number of relatively short time series acquired from functional Magnetic Resonance Imaging (fMRI) data of the human brain.
format	article
author	Axel Wismüller Adora M. Dsouza M. Ali Vosoughi Anas Abidin
author_facet	Axel Wismüller Adora M. Dsouza M. Ali Vosoughi Anas Abidin
author_sort	Axel Wismüller
title	Large-scale nonlinear Granger causality for inferring directed dependence from short multivariate time-series data
title_short	Large-scale nonlinear Granger causality for inferring directed dependence from short multivariate time-series data
title_full	Large-scale nonlinear Granger causality for inferring directed dependence from short multivariate time-series data
title_fullStr	Large-scale nonlinear Granger causality for inferring directed dependence from short multivariate time-series data
title_full_unstemmed	Large-scale nonlinear Granger causality for inferring directed dependence from short multivariate time-series data
title_sort	large-scale nonlinear granger causality for inferring directed dependence from short multivariate time-series data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f25323c5047c44b7a97ce17913dbbeed
work_keys_str_mv	AT axelwismuller largescalenonlineargrangercausalityforinferringdirecteddependencefromshortmultivariatetimeseriesdata AT adoramdsouza largescalenonlineargrangercausalityforinferringdirecteddependencefromshortmultivariatetimeseriesdata AT malivosoughi largescalenonlineargrangercausalityforinferringdirecteddependencefromshortmultivariatetimeseriesdata AT anasabidin largescalenonlineargrangercausalityforinferringdirecteddependencefromshortmultivariatetimeseriesdata
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Large-scale nonlinear Granger causality for inferring directed dependence from short multivariate time-series data

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