Constructing ordinal partition transition networks from multivariate time series

Abstract A growing number of algorithms have been proposed to map a scalar time series into ordinal partition transition networks. However, most observable phenomena in the empirical sciences are of a multivariate nature. We construct ordinal partition transition networks for multivariate time serie...

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Autores principales:	Jiayang Zhang, Jie Zhou, Ming Tang, Heng Guo, Michael Small, Yong Zou
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/fe9b482bc4d6410e809cb677e3224b9c
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spelling	oai:doaj.org-article:fe9b482bc4d6410e809cb677e3224b9c2021-12-02T11:41:11ZConstructing ordinal partition transition networks from multivariate time series10.1038/s41598-017-08245-x2045-2322https://doaj.org/article/fe9b482bc4d6410e809cb677e3224b9c2017-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-08245-xhttps://doaj.org/toc/2045-2322Abstract A growing number of algorithms have been proposed to map a scalar time series into ordinal partition transition networks. However, most observable phenomena in the empirical sciences are of a multivariate nature. We construct ordinal partition transition networks for multivariate time series. This approach yields weighted directed networks representing the pattern transition properties of time series in velocity space, which hence provides dynamic insights of the underling system. Furthermore, we propose a measure of entropy to characterize ordinal partition transition dynamics, which is sensitive to capturing the possible local geometric changes of phase space trajectories. We demonstrate the applicability of pattern transition networks to capture phase coherence to non-coherence transitions, and to characterize paths to phase synchronizations. Therefore, we conclude that the ordinal partition transition network approach provides complementary insight to the traditional symbolic analysis of nonlinear multivariate time series.Jiayang ZhangJie ZhouMing TangHeng GuoMichael SmallYong ZouNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-13 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jiayang Zhang Jie Zhou Ming Tang Heng Guo Michael Small Yong Zou Constructing ordinal partition transition networks from multivariate time series
description	Abstract A growing number of algorithms have been proposed to map a scalar time series into ordinal partition transition networks. However, most observable phenomena in the empirical sciences are of a multivariate nature. We construct ordinal partition transition networks for multivariate time series. This approach yields weighted directed networks representing the pattern transition properties of time series in velocity space, which hence provides dynamic insights of the underling system. Furthermore, we propose a measure of entropy to characterize ordinal partition transition dynamics, which is sensitive to capturing the possible local geometric changes of phase space trajectories. We demonstrate the applicability of pattern transition networks to capture phase coherence to non-coherence transitions, and to characterize paths to phase synchronizations. Therefore, we conclude that the ordinal partition transition network approach provides complementary insight to the traditional symbolic analysis of nonlinear multivariate time series.
format	article
author	Jiayang Zhang Jie Zhou Ming Tang Heng Guo Michael Small Yong Zou
author_facet	Jiayang Zhang Jie Zhou Ming Tang Heng Guo Michael Small Yong Zou
author_sort	Jiayang Zhang
title	Constructing ordinal partition transition networks from multivariate time series
title_short	Constructing ordinal partition transition networks from multivariate time series
title_full	Constructing ordinal partition transition networks from multivariate time series
title_fullStr	Constructing ordinal partition transition networks from multivariate time series
title_full_unstemmed	Constructing ordinal partition transition networks from multivariate time series
title_sort	constructing ordinal partition transition networks from multivariate time series
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/fe9b482bc4d6410e809cb677e3224b9c
work_keys_str_mv	AT jiayangzhang constructingordinalpartitiontransitionnetworksfrommultivariatetimeseries AT jiezhou constructingordinalpartitiontransitionnetworksfrommultivariatetimeseries AT mingtang constructingordinalpartitiontransitionnetworksfrommultivariatetimeseries AT hengguo constructingordinalpartitiontransitionnetworksfrommultivariatetimeseries AT michaelsmall constructingordinalpartitiontransitionnetworksfrommultivariatetimeseries AT yongzou constructingordinalpartitiontransitionnetworksfrommultivariatetimeseries
_version_	1718395498885283840

Constructing ordinal partition transition networks from multivariate time series

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