Distinguishing noise from chaos

Autores
Rosso, Osvaldo A.; Larrondo, Hilda Ángela; Martín, María Teresa; Plastino, Ángel Luis; Fuentes, Miguel A.
Año de publicación
2007
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Chaotic systems share with stochastic processes several properties that make them almost undistinguishable. In this communication we introduce a representation space, to be called the complexity-entropy causality plane. Its horizontal and vertical axis are suitable functionals of the pertinent probability distribution, namely, the entropy of the system and an appropriate statistical complexity measure, respectively. These two functionals are evaluated using the Bandt-Pompe recipe to assign a probability distribution function to the time series generated by the system. Several well-known model-generated time series, usually regarded as being of either stochastic or chaotic nature, are analyzed so as to illustrate the approach. The main achievement of this communication is the possibility of clearly distinguishing between them in our representation space, something that is rather difficult otherwise.
Instituto de Física La Plata
Materia
Física
Ciencias Exactas
Chaotic systems
Entropy (information theory)
Representation space
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/126151
Acceder
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repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Distinguishing noise from chaosRosso, Osvaldo A.Larrondo, Hilda ÁngelaMartín, María TeresaPlastino, Ángel LuisFuentes, Miguel A.FísicaCiencias ExactasChaotic systemsEntropy (information theory)Representation spaceChaotic systems share with stochastic processes several properties that make them almost undistinguishable. In this communication we introduce a representation space, to be called the complexity-entropy causality plane. Its horizontal and vertical axis are suitable functionals of the pertinent probability distribution, namely, the entropy of the system and an appropriate statistical complexity measure, respectively. These two functionals are evaluated using the Bandt-Pompe recipe to assign a probability distribution function to the time series generated by the system. Several well-known model-generated time series, usually regarded as being of either stochastic or chaotic nature, are analyzed so as to illustrate the approach. The main achievement of this communication is the possibility of clearly distinguishing between them in our representation space, something that is rather difficult otherwise.Instituto de Física La Plata2007-10-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/126151enginfo:eu-repo/semantics/altIdentifier/url/https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.99.154102info:eu-repo/semantics/altIdentifier/issn/0031-9007info:eu-repo/semantics/altIdentifier/issn/1079-7114info:eu-repo/semantics/altIdentifier/pmid/17995170info:eu-repo/semantics/altIdentifier/doi/10.1103/physrevlett.99.154102info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International (CC BY 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-22T17:11:16Zoai:sedici.unlp.edu.ar:10915/126151Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-22 17:11:16.287SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Distinguishing noise from chaos
title Distinguishing noise from chaos
spellingShingle Distinguishing noise from chaos
Rosso, Osvaldo A.
Física
Ciencias Exactas
Chaotic systems
Entropy (information theory)
Representation space
title_short Distinguishing noise from chaos
title_full Distinguishing noise from chaos
title_fullStr Distinguishing noise from chaos
title_full_unstemmed Distinguishing noise from chaos
title_sort Distinguishing noise from chaos
dc.creator.none.fl_str_mv Rosso, Osvaldo A.
Larrondo, Hilda Ángela
Martín, María Teresa
Plastino, Ángel Luis
Fuentes, Miguel A.
author Rosso, Osvaldo A.
author_facet Rosso, Osvaldo A.
Larrondo, Hilda Ángela
Martín, María Teresa
Plastino, Ángel Luis
Fuentes, Miguel A.
author_role author
author2 Larrondo, Hilda Ángela
Martín, María Teresa
Plastino, Ángel Luis
Fuentes, Miguel A.
author2_role author
author
author
author
dc.subject.none.fl_str_mv Física
Ciencias Exactas
Chaotic systems
Entropy (information theory)
Representation space
topic Física
Ciencias Exactas
Chaotic systems
Entropy (information theory)
Representation space
dc.description.none.fl_txt_mv Chaotic systems share with stochastic processes several properties that make them almost undistinguishable. In this communication we introduce a representation space, to be called the complexity-entropy causality plane. Its horizontal and vertical axis are suitable functionals of the pertinent probability distribution, namely, the entropy of the system and an appropriate statistical complexity measure, respectively. These two functionals are evaluated using the Bandt-Pompe recipe to assign a probability distribution function to the time series generated by the system. Several well-known model-generated time series, usually regarded as being of either stochastic or chaotic nature, are analyzed so as to illustrate the approach. The main achievement of this communication is the possibility of clearly distinguishing between them in our representation space, something that is rather difficult otherwise.
Instituto de Física La Plata
description Chaotic systems share with stochastic processes several properties that make them almost undistinguishable. In this communication we introduce a representation space, to be called the complexity-entropy causality plane. Its horizontal and vertical axis are suitable functionals of the pertinent probability distribution, namely, the entropy of the system and an appropriate statistical complexity measure, respectively. These two functionals are evaluated using the Bandt-Pompe recipe to assign a probability distribution function to the time series generated by the system. Several well-known model-generated time series, usually regarded as being of either stochastic or chaotic nature, are analyzed so as to illustrate the approach. The main achievement of this communication is the possibility of clearly distinguishing between them in our representation space, something that is rather difficult otherwise.
publishDate 2007
dc.date.none.fl_str_mv 2007-10-12
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
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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://sedici.unlp.edu.ar/handle/10915/126151
url http://sedici.unlp.edu.ar/handle/10915/126151
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.99.154102
info:eu-repo/semantics/altIdentifier/issn/0031-9007
info:eu-repo/semantics/altIdentifier/issn/1079-7114
info:eu-repo/semantics/altIdentifier/pmid/17995170
info:eu-repo/semantics/altIdentifier/doi/10.1103/physrevlett.99.154102
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
Creative Commons Attribution 4.0 International (CC BY 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
Creative Commons Attribution 4.0 International (CC BY 4.0)
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
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