Matlab Open Source Code: Noise-Assisted Multivariate Empirical Mode Decomposition Based Causal Decomposition for Causality Inference of Bivariate Time Series.

Zhang, Y; Wang, G; Li, Z; Xie, M; Celler, B; Su, S; Xu, P; Yao, D

Matlab Open Source Code: Noise-Assisted Multivariate Empirical Mode Decomposition Based Causal Decomposition for Causality Inference of Bivariate Time Series.

Zhang, Y Wang, G Li, Z Xie, M Celler, B Su, S

Xu, P Yao, D

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Publisher:: Frontiers
Publication Type:: Journal Article
Citation:: Front Neuroinform, 2022, 16, pp. 851645
Issue Date:: 2022

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Field	Value	Language
dc.contributor.author	Zhang, Y
dc.contributor.author	Wang, G
dc.contributor.author	Li, Z
dc.contributor.author	Xie, M
dc.contributor.author	Celler, B
dc.contributor.author	Su, S https://orcid.org/0000-0002-5720-8852
dc.contributor.author	Xu, P
dc.contributor.author	Yao, D
dc.date.accessioned	2023-03-22T00:33:38Z
dc.date.available	2022-05-04
dc.date.available	2023-03-22T00:33:38Z
dc.date.issued	2022
dc.identifier.citation	Front Neuroinform, 2022, 16, pp. 851645
dc.identifier.issn	1662-5196
dc.identifier.issn	1662-5196
dc.identifier.uri	http://hdl.handle.net/10453/168019
dc.description.abstract	Causality inference has arrested much attention in academic studies. Currently, multiple methods such as Granger causality, Convergent Cross Mapping (CCM), and Noise-assisted Multivariate Empirical Mode Decomposition (NA-MEMD) are introduced to solve the problem. Motivated by the researchers who uploaded the open-source code for causality inference, we hereby present the Matlab code of NA-MEMD Causal Decomposition to help users implement the algorithm in multiple scenarios. The code is developed on Matlab2020 and is mainly divided into three subfunctions: na_memd, Plseries, and cd_na_memd. na_memd is called in the main function to generate the matrix of Intrinsic Mode Functions (IMFs) and Plseries can display the average frequency and phase difference of IMFs of the same order in a matrix which can be used for the selection of the main Intrinsic Causal Component (ICC) and ICCs set. cd_na_memd is called to perform causal redecomposition after removing the main ICC from the original time series and output the result of NA-MEMD Causal Decomposition. The performance of the code is evaluated from the perspective of executing time, robustness, and validity. With the data amount enlarging, the executing time increases linearly with it and the value of causal strength oscillates in an ideally small interval which represents the relatively high robustness of the code. The validity is verified based on the open-access predator-prey data (wolf-moose bivariate time series from Isle Royale National Park in Michigan, USA) and our result is aligned with that of Causal Decomposition.
dc.format	Electronic-eCollection
dc.language	eng
dc.publisher	Frontiers
dc.relation.ispartof	Front Neuroinform
dc.relation.isbasedon	10.3389/fninf.2022.851645
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	1109 Neurosciences, 1702 Cognitive Sciences
dc.title	Matlab Open Source Code: Noise-Assisted Multivariate Empirical Mode Decomposition Based Causal Decomposition for Causality Inference of Bivariate Time Series.
dc.type	Journal Article
utslib.citation.volume	16
utslib.location.activity	Switzerland
utslib.for	1109 Neurosciences
utslib.for	1702 Cognitive Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Centre for Health Technologies (CHT)
utslib.copyright.status	open_access	*
dc.date.updated	2023-03-22T00:33:27Z
pubs.publication-status	Published online
pubs.volume	16

Abstract:

Causality inference has arrested much attention in academic studies. Currently, multiple methods such as Granger causality, Convergent Cross Mapping (CCM), and Noise-assisted Multivariate Empirical Mode Decomposition (NA-MEMD) are introduced to solve the problem. Motivated by the researchers who uploaded the open-source code for causality inference, we hereby present the Matlab code of NA-MEMD Causal Decomposition to help users implement the algorithm in multiple scenarios. The code is developed on Matlab2020 and is mainly divided into three subfunctions: na_memd, Plseries, and cd_na_memd. na_memd is called in the main function to generate the matrix of Intrinsic Mode Functions (IMFs) and Plseries can display the average frequency and phase difference of IMFs of the same order in a matrix which can be used for the selection of the main Intrinsic Causal Component (ICC) and ICCs set. cd_na_memd is called to perform causal redecomposition after removing the main ICC from the original time series and output the result of NA-MEMD Causal Decomposition. The performance of the code is evaluated from the perspective of executing time, robustness, and validity. With the data amount enlarging, the executing time increases linearly with it and the value of causal strength oscillates in an ideally small interval which represents the relatively high robustness of the code. The validity is verified based on the open-access predator-prey data (wolf-moose bivariate time series from Isle Royale National Park in Michigan, USA) and our result is aligned with that of Causal Decomposition.

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http://hdl.handle.net/10453/168019