Ordinal patterns-based methodologies for distinguishing chaos from noise in discrete time series

Here, Zanin and Olivares review the permutation patterns-based metrics used to distinguish chaos from stochasticity in discrete time series. They analyse their performance and computational cost, and compare their applicability to real-world time series.

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Detalles Bibliográficos
Autores principales:	Massimiliano Zanin, Felipe Olivares
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Astrophysics QB460-466 Physics QC1-999
Acceso en línea:	https://doaj.org/article/0d10f3333557449fbd1dd92ccc4eb942
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spelling	oai:doaj.org-article:0d10f3333557449fbd1dd92ccc4eb9422021-12-02T18:51:53ZOrdinal patterns-based methodologies for distinguishing chaos from noise in discrete time series10.1038/s42005-021-00696-z2399-3650https://doaj.org/article/0d10f3333557449fbd1dd92ccc4eb9422021-08-01T00:00:00Zhttps://doi.org/10.1038/s42005-021-00696-zhttps://doaj.org/toc/2399-3650Here, Zanin and Olivares review the permutation patterns-based metrics used to distinguish chaos from stochasticity in discrete time series. They analyse their performance and computational cost, and compare their applicability to real-world time series.Massimiliano ZaninFelipe OlivaresNature PortfolioarticleAstrophysicsQB460-466PhysicsQC1-999ENCommunications Physics, Vol 4, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Astrophysics QB460-466 Physics QC1-999
spellingShingle	Astrophysics QB460-466 Physics QC1-999 Massimiliano Zanin Felipe Olivares Ordinal patterns-based methodologies for distinguishing chaos from noise in discrete time series
description	Here, Zanin and Olivares review the permutation patterns-based metrics used to distinguish chaos from stochasticity in discrete time series. They analyse their performance and computational cost, and compare their applicability to real-world time series.
format	article
author	Massimiliano Zanin Felipe Olivares
author_facet	Massimiliano Zanin Felipe Olivares
author_sort	Massimiliano Zanin
title	Ordinal patterns-based methodologies for distinguishing chaos from noise in discrete time series
title_short	Ordinal patterns-based methodologies for distinguishing chaos from noise in discrete time series
title_full	Ordinal patterns-based methodologies for distinguishing chaos from noise in discrete time series
title_fullStr	Ordinal patterns-based methodologies for distinguishing chaos from noise in discrete time series
title_full_unstemmed	Ordinal patterns-based methodologies for distinguishing chaos from noise in discrete time series
title_sort	ordinal patterns-based methodologies for distinguishing chaos from noise in discrete time series
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/0d10f3333557449fbd1dd92ccc4eb942
work_keys_str_mv	AT massimilianozanin ordinalpatternsbasedmethodologiesfordistinguishingchaosfromnoiseindiscretetimeseries AT felipeolivares ordinalpatternsbasedmethodologiesfordistinguishingchaosfromnoiseindiscretetimeseries
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Ordinal patterns-based methodologies for distinguishing chaos from noise in discrete time series

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