Characterization of time series via Rényi complexity–entropy curves

Autores
Jauregui, Max; Zunino, Luciano José; Lenzi, Ervin K.; dos Santos Mendes, Reino; Ribeiro, Haroldo Valentín
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
One of the most useful tools for distinguishing between chaotic and stochastic time series is the so-called complexity-entropy causality plane. This diagram involves two complexity measures: the Shannon entropy and the statistical complexity. Recently, this idea has been generalized by considering the Tsallis monoparametric generalization of the Shannon entropy, yielding complexity-entropy curves. These curves have proven to enhance the discrimination among different time series related to stochastic and chaotic processes of numerical and experimental nature. Here we further explore these complexity-entropy curves in the context of the Rényi entropy, which is another monoparametric generalization of the Shannon entropy. By combining the Rényi entropy with the proper generalization of the statistical complexity, we associate a parametric curve (the Rényi complexity-entropy curve) with a given time series. We explore this approach in a series of numerical and experimental applications, demonstrating the usefulness of this new technique for time series analysis. We show that the Rényi complexity-entropy curves enable the differentiation among time series of chaotic, stochastic, and periodic nature. In particular, time series of stochastic nature are associated with curves displaying positive curvature in a neighborhood of their initial points, whereas curves related to chaotic phenomena have a negative curvature; finally, periodic time series are represented by vertical straight lines.
Fil: Jauregui, Max. Universidade Estadual de Maringá. Departamento de Física; Brasil
Fil: Zunino, Luciano José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigaciones Ópticas. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones Ópticas. Universidad Nacional de La Plata. Centro de Investigaciones Ópticas; Argentina
Fil: Lenzi, Ervin K.. Universidade Estadual de Ponta Grossa. Departamento de Física; Brasil
Fil: dos Santos Mendes, Reino. Universidade Estadual de Maringá. Departamento de Física; Brasil
Fil: Ribeiro, Haroldo Valentín. Universidade Estadual de Maringá. Departamento de Física; Brasil
Materia
TIME SERIES
RÉNYI ENTROPY
COMPLEXITY MEASURES
ORDINAL PATTERNS PROBABILITIES
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/89294
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Characterization of time series via Rényi complexity–entropy curvesJauregui, MaxZunino, Luciano JoséLenzi, Ervin K.dos Santos Mendes, ReinoRibeiro, Haroldo ValentínTIME SERIESRÉNYI ENTROPYCOMPLEXITY MEASURESORDINAL PATTERNS PROBABILITIEShttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1One of the most useful tools for distinguishing between chaotic and stochastic time series is the so-called complexity-entropy causality plane. This diagram involves two complexity measures: the Shannon entropy and the statistical complexity. Recently, this idea has been generalized by considering the Tsallis monoparametric generalization of the Shannon entropy, yielding complexity-entropy curves. These curves have proven to enhance the discrimination among different time series related to stochastic and chaotic processes of numerical and experimental nature. Here we further explore these complexity-entropy curves in the context of the Rényi entropy, which is another monoparametric generalization of the Shannon entropy. By combining the Rényi entropy with the proper generalization of the statistical complexity, we associate a parametric curve (the Rényi complexity-entropy curve) with a given time series. We explore this approach in a series of numerical and experimental applications, demonstrating the usefulness of this new technique for time series analysis. We show that the Rényi complexity-entropy curves enable the differentiation among time series of chaotic, stochastic, and periodic nature. In particular, time series of stochastic nature are associated with curves displaying positive curvature in a neighborhood of their initial points, whereas curves related to chaotic phenomena have a negative curvature; finally, periodic time series are represented by vertical straight lines.Fil: Jauregui, Max. Universidade Estadual de Maringá. Departamento de Física; BrasilFil: Zunino, Luciano José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigaciones Ópticas. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones Ópticas. Universidad Nacional de La Plata. Centro de Investigaciones Ópticas; ArgentinaFil: Lenzi, Ervin K.. Universidade Estadual de Ponta Grossa. Departamento de Física; BrasilFil: dos Santos Mendes, Reino. Universidade Estadual de Maringá. Departamento de Física; BrasilFil: Ribeiro, Haroldo Valentín. Universidade Estadual de Maringá. Departamento de Física; BrasilElsevier Science2018-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/89294Jauregui, Max; Zunino, Luciano José; Lenzi, Ervin K.; dos Santos Mendes, Reino; Ribeiro, Haroldo Valentín; Characterization of time series via Rényi complexity–entropy curves; Elsevier Science; Physica A: Statistical Mechanics and its Applications; 498; 5-2018; 74-850378-4371CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://linkinghub.elsevier.com/retrieve/pii/S0378437118300463info:eu-repo/semantics/altIdentifier/doi/10.1016/j.physa.2018.01.026info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:46:20Zoai:ri.conicet.gov.ar:11336/89294instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:46:20.31CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Characterization of time series via Rényi complexity–entropy curves
title Characterization of time series via Rényi complexity–entropy curves
spellingShingle Characterization of time series via Rényi complexity–entropy curves
Jauregui, Max
TIME SERIES
RÉNYI ENTROPY
COMPLEXITY MEASURES
ORDINAL PATTERNS PROBABILITIES
title_short Characterization of time series via Rényi complexity–entropy curves
title_full Characterization of time series via Rényi complexity–entropy curves
title_fullStr Characterization of time series via Rényi complexity–entropy curves
title_full_unstemmed Characterization of time series via Rényi complexity–entropy curves
title_sort Characterization of time series via Rényi complexity–entropy curves
dc.creator.none.fl_str_mv Jauregui, Max
Zunino, Luciano José
Lenzi, Ervin K.
dos Santos Mendes, Reino
Ribeiro, Haroldo Valentín
author Jauregui, Max
author_facet Jauregui, Max
Zunino, Luciano José
Lenzi, Ervin K.
dos Santos Mendes, Reino
Ribeiro, Haroldo Valentín
author_role author
author2 Zunino, Luciano José
Lenzi, Ervin K.
dos Santos Mendes, Reino
Ribeiro, Haroldo Valentín
author2_role author
author
author
author
dc.subject.none.fl_str_mv TIME SERIES
RÉNYI ENTROPY
COMPLEXITY MEASURES
ORDINAL PATTERNS PROBABILITIES
topic TIME SERIES
RÉNYI ENTROPY
COMPLEXITY MEASURES
ORDINAL PATTERNS PROBABILITIES
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv One of the most useful tools for distinguishing between chaotic and stochastic time series is the so-called complexity-entropy causality plane. This diagram involves two complexity measures: the Shannon entropy and the statistical complexity. Recently, this idea has been generalized by considering the Tsallis monoparametric generalization of the Shannon entropy, yielding complexity-entropy curves. These curves have proven to enhance the discrimination among different time series related to stochastic and chaotic processes of numerical and experimental nature. Here we further explore these complexity-entropy curves in the context of the Rényi entropy, which is another monoparametric generalization of the Shannon entropy. By combining the Rényi entropy with the proper generalization of the statistical complexity, we associate a parametric curve (the Rényi complexity-entropy curve) with a given time series. We explore this approach in a series of numerical and experimental applications, demonstrating the usefulness of this new technique for time series analysis. We show that the Rényi complexity-entropy curves enable the differentiation among time series of chaotic, stochastic, and periodic nature. In particular, time series of stochastic nature are associated with curves displaying positive curvature in a neighborhood of their initial points, whereas curves related to chaotic phenomena have a negative curvature; finally, periodic time series are represented by vertical straight lines.
Fil: Jauregui, Max. Universidade Estadual de Maringá. Departamento de Física; Brasil
Fil: Zunino, Luciano José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigaciones Ópticas. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones Ópticas. Universidad Nacional de La Plata. Centro de Investigaciones Ópticas; Argentina
Fil: Lenzi, Ervin K.. Universidade Estadual de Ponta Grossa. Departamento de Física; Brasil
Fil: dos Santos Mendes, Reino. Universidade Estadual de Maringá. Departamento de Física; Brasil
Fil: Ribeiro, Haroldo Valentín. Universidade Estadual de Maringá. Departamento de Física; Brasil
description One of the most useful tools for distinguishing between chaotic and stochastic time series is the so-called complexity-entropy causality plane. This diagram involves two complexity measures: the Shannon entropy and the statistical complexity. Recently, this idea has been generalized by considering the Tsallis monoparametric generalization of the Shannon entropy, yielding complexity-entropy curves. These curves have proven to enhance the discrimination among different time series related to stochastic and chaotic processes of numerical and experimental nature. Here we further explore these complexity-entropy curves in the context of the Rényi entropy, which is another monoparametric generalization of the Shannon entropy. By combining the Rényi entropy with the proper generalization of the statistical complexity, we associate a parametric curve (the Rényi complexity-entropy curve) with a given time series. We explore this approach in a series of numerical and experimental applications, demonstrating the usefulness of this new technique for time series analysis. We show that the Rényi complexity-entropy curves enable the differentiation among time series of chaotic, stochastic, and periodic nature. In particular, time series of stochastic nature are associated with curves displaying positive curvature in a neighborhood of their initial points, whereas curves related to chaotic phenomena have a negative curvature; finally, periodic time series are represented by vertical straight lines.
publishDate 2018
dc.date.none.fl_str_mv 2018-05
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/89294
Jauregui, Max; Zunino, Luciano José; Lenzi, Ervin K.; dos Santos Mendes, Reino; Ribeiro, Haroldo Valentín; Characterization of time series via Rényi complexity–entropy curves; Elsevier Science; Physica A: Statistical Mechanics and its Applications; 498; 5-2018; 74-85
0378-4371
CONICET Digital
CONICET
url http://hdl.handle.net/11336/89294
identifier_str_mv Jauregui, Max; Zunino, Luciano José; Lenzi, Ervin K.; dos Santos Mendes, Reino; Ribeiro, Haroldo Valentín; Characterization of time series via Rényi complexity–entropy curves; Elsevier Science; Physica A: Statistical Mechanics and its Applications; 498; 5-2018; 74-85
0378-4371
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://linkinghub.elsevier.com/retrieve/pii/S0378437118300463
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.physa.2018.01.026
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.13397

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