Incorporation of causality structures to complex network analysis of time-varying behaviour of multivariate time series

Abstract This paper presents a new methodology for characterising the evolving behaviour of the time-varying causality between multivariate time series, from the perspective of change in the structure of the causality pattern. We propose that such evolutionary behaviour should be tracked by means of...

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Autores principales:	Leo Carlos-Sandberg, Christopher D. Clack
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5a756776cb0f46ce8a626619a361f9b0
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spelling	oai:doaj.org-article:5a756776cb0f46ce8a626619a361f9b02021-12-02T17:26:55ZIncorporation of causality structures to complex network analysis of time-varying behaviour of multivariate time series10.1038/s41598-021-97741-22045-2322https://doaj.org/article/5a756776cb0f46ce8a626619a361f9b02021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97741-2https://doaj.org/toc/2045-2322Abstract This paper presents a new methodology for characterising the evolving behaviour of the time-varying causality between multivariate time series, from the perspective of change in the structure of the causality pattern. We propose that such evolutionary behaviour should be tracked by means of a complex network whose nodes are causality patterns and edges are transitions between those patterns of causality. In our new methodology each edge has a weight that includes the frequency of the given transition and two metrics relating to the gross and net structural change in causality pattern, which we call $$\alpha$$ α and $$\beta$$ β . To characterise aspects of the behaviour within this network, five approaches are presented and motivated. To act as a demonstration of this methodology an application of sample data from the international oil market is presented. This example illustrates how our new methodology is able to extract information about evolving causality behaviour. For example, it reveals non-random time-varying behaviour that favours transitions resulting in predominantly similar causality patterns, and it discovers clustering of similar causality patterns and some transitional behaviour between these clusters. The example illustrates how our new methodology supports the inference that the evolution of causality in the system is related to the addition or removal of a few causality links, primarily keeping a similar causality pattern, and that the evolution is not related to some other measure such as the overall number of causality links.Leo Carlos-SandbergChristopher D. ClackNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-16 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Leo Carlos-Sandberg Christopher D. Clack Incorporation of causality structures to complex network analysis of time-varying behaviour of multivariate time series
description	Abstract This paper presents a new methodology for characterising the evolving behaviour of the time-varying causality between multivariate time series, from the perspective of change in the structure of the causality pattern. We propose that such evolutionary behaviour should be tracked by means of a complex network whose nodes are causality patterns and edges are transitions between those patterns of causality. In our new methodology each edge has a weight that includes the frequency of the given transition and two metrics relating to the gross and net structural change in causality pattern, which we call $$\alpha$$ α and $$\beta$$ β . To characterise aspects of the behaviour within this network, five approaches are presented and motivated. To act as a demonstration of this methodology an application of sample data from the international oil market is presented. This example illustrates how our new methodology is able to extract information about evolving causality behaviour. For example, it reveals non-random time-varying behaviour that favours transitions resulting in predominantly similar causality patterns, and it discovers clustering of similar causality patterns and some transitional behaviour between these clusters. The example illustrates how our new methodology supports the inference that the evolution of causality in the system is related to the addition or removal of a few causality links, primarily keeping a similar causality pattern, and that the evolution is not related to some other measure such as the overall number of causality links.
format	article
author	Leo Carlos-Sandberg Christopher D. Clack
author_facet	Leo Carlos-Sandberg Christopher D. Clack
author_sort	Leo Carlos-Sandberg
title	Incorporation of causality structures to complex network analysis of time-varying behaviour of multivariate time series
title_short	Incorporation of causality structures to complex network analysis of time-varying behaviour of multivariate time series
title_full	Incorporation of causality structures to complex network analysis of time-varying behaviour of multivariate time series
title_fullStr	Incorporation of causality structures to complex network analysis of time-varying behaviour of multivariate time series
title_full_unstemmed	Incorporation of causality structures to complex network analysis of time-varying behaviour of multivariate time series
title_sort	incorporation of causality structures to complex network analysis of time-varying behaviour of multivariate time series
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/5a756776cb0f46ce8a626619a361f9b0
work_keys_str_mv	AT leocarlossandberg incorporationofcausalitystructurestocomplexnetworkanalysisoftimevaryingbehaviourofmultivariatetimeseries AT christopherdclack incorporationofcausalitystructurestocomplexnetworkanalysisoftimevaryingbehaviourofmultivariatetimeseries
_version_	1718380790132244480

Incorporation of causality structures to complex network analysis of time-varying behaviour of multivariate time series

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