The asymptotic distribution of the permutation entropy

Autores
Rey, Andrea Alejandra; Frery, A. C.; Gambini, J.; Lucini, María Magdalena
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Ordinal patterns serve as a robust symbolic transformation technique, enabling the unveiling of latent dynamics within time series data. This methodology involves constructing histograms of patterns, followed by the calculation of both entropy and statistical complexity—an avenue yet to be fully understood in terms of its statistical properties. While asymptotic results can be derived by assuming a multinomial distribution for histogram proportions, the challenge emerges from the non-independence present in the sequence of ordinal patterns. Consequently, the direct application of the multinomial assumption is questionable. This study focuses on the computation of the asymptotic distribution of permutation entropy, considering the inherent patterns’ correlation structure. Furthermore, the research delves into a comparative analysis, pitting this distribution against the entropy derived from a multinomial law. We present simulation algorithms for sampling time series with prescribed histograms of patterns and transition probabilities between them. Through this analysis, we better understand the intricacies of ordinal patterns and their statistical attributes.
Fil: Rey, Andrea Alejandra. Secretaria de Investigacion ; Universidad Nacional de Hurlingham; . Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Frery, A. C.. Victoria University Of Wellington; Nueva Zelanda
Fil: Gambini, J.. Universidad Nacional de Hurlingham.; Argentina
Fil: Lucini, María Magdalena. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas y Naturales y Agrimensura; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; Argentina
Materia
ENTROPY
PROBABILITY THEORY
COVARIANCE AND CORRELATION
STATISTICAL ANALYSIS
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:11336/224578
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling The asymptotic distribution of the permutation entropyRey, Andrea AlejandraFrery, A. C.Gambini, J.Lucini, María MagdalenaENTROPYPROBABILITY THEORYCOVARIANCE AND CORRELATIONSTATISTICAL ANALYSIShttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1https://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1Ordinal patterns serve as a robust symbolic transformation technique, enabling the unveiling of latent dynamics within time series data. This methodology involves constructing histograms of patterns, followed by the calculation of both entropy and statistical complexity—an avenue yet to be fully understood in terms of its statistical properties. While asymptotic results can be derived by assuming a multinomial distribution for histogram proportions, the challenge emerges from the non-independence present in the sequence of ordinal patterns. Consequently, the direct application of the multinomial assumption is questionable. This study focuses on the computation of the asymptotic distribution of permutation entropy, considering the inherent patterns’ correlation structure. Furthermore, the research delves into a comparative analysis, pitting this distribution against the entropy derived from a multinomial law. We present simulation algorithms for sampling time series with prescribed histograms of patterns and transition probabilities between them. Through this analysis, we better understand the intricacies of ordinal patterns and their statistical attributes.Fil: Rey, Andrea Alejandra. Secretaria de Investigacion ; Universidad Nacional de Hurlingham; . Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Frery, A. C.. Victoria University Of Wellington; Nueva ZelandaFil: Gambini, J.. Universidad Nacional de Hurlingham.; ArgentinaFil: Lucini, María Magdalena. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas y Naturales y Agrimensura; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; ArgentinaAmerican Institute of Physics2023-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/224578Rey, Andrea Alejandra; Frery, A. C.; Gambini, J.; Lucini, María Magdalena; The asymptotic distribution of the permutation entropy; American Institute of Physics; Chaos; 33; 11; 11-2023; 1-251054-1500CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://pubs.aip.org/cha/article/33/11/113108/2919291/The-asymptotic-distribution-of-the-permutationinfo:eu-repo/semantics/altIdentifier/doi/10.1063/5.0171508info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:37:35Zoai:ri.conicet.gov.ar:11336/224578instacron: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:34982025-09-29 09:37:35.915CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv The asymptotic distribution of the permutation entropy
title The asymptotic distribution of the permutation entropy
spellingShingle The asymptotic distribution of the permutation entropy
Rey, Andrea Alejandra
ENTROPY
PROBABILITY THEORY
COVARIANCE AND CORRELATION
STATISTICAL ANALYSIS
title_short The asymptotic distribution of the permutation entropy
title_full The asymptotic distribution of the permutation entropy
title_fullStr The asymptotic distribution of the permutation entropy
title_full_unstemmed The asymptotic distribution of the permutation entropy
title_sort The asymptotic distribution of the permutation entropy
dc.creator.none.fl_str_mv Rey, Andrea Alejandra
Frery, A. C.
Gambini, J.
Lucini, María Magdalena
author Rey, Andrea Alejandra
author_facet Rey, Andrea Alejandra
Frery, A. C.
Gambini, J.
Lucini, María Magdalena
author_role author
author2 Frery, A. C.
Gambini, J.
Lucini, María Magdalena
author2_role author
author
author
dc.subject.none.fl_str_mv ENTROPY
PROBABILITY THEORY
COVARIANCE AND CORRELATION
STATISTICAL ANALYSIS
topic ENTROPY
PROBABILITY THEORY
COVARIANCE AND CORRELATION
STATISTICAL ANALYSIS
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Ordinal patterns serve as a robust symbolic transformation technique, enabling the unveiling of latent dynamics within time series data. This methodology involves constructing histograms of patterns, followed by the calculation of both entropy and statistical complexity—an avenue yet to be fully understood in terms of its statistical properties. While asymptotic results can be derived by assuming a multinomial distribution for histogram proportions, the challenge emerges from the non-independence present in the sequence of ordinal patterns. Consequently, the direct application of the multinomial assumption is questionable. This study focuses on the computation of the asymptotic distribution of permutation entropy, considering the inherent patterns’ correlation structure. Furthermore, the research delves into a comparative analysis, pitting this distribution against the entropy derived from a multinomial law. We present simulation algorithms for sampling time series with prescribed histograms of patterns and transition probabilities between them. Through this analysis, we better understand the intricacies of ordinal patterns and their statistical attributes.
Fil: Rey, Andrea Alejandra. Secretaria de Investigacion ; Universidad Nacional de Hurlingham; . Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Frery, A. C.. Victoria University Of Wellington; Nueva Zelanda
Fil: Gambini, J.. Universidad Nacional de Hurlingham.; Argentina
Fil: Lucini, María Magdalena. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas y Naturales y Agrimensura; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; Argentina
description Ordinal patterns serve as a robust symbolic transformation technique, enabling the unveiling of latent dynamics within time series data. This methodology involves constructing histograms of patterns, followed by the calculation of both entropy and statistical complexity—an avenue yet to be fully understood in terms of its statistical properties. While asymptotic results can be derived by assuming a multinomial distribution for histogram proportions, the challenge emerges from the non-independence present in the sequence of ordinal patterns. Consequently, the direct application of the multinomial assumption is questionable. This study focuses on the computation of the asymptotic distribution of permutation entropy, considering the inherent patterns’ correlation structure. Furthermore, the research delves into a comparative analysis, pitting this distribution against the entropy derived from a multinomial law. We present simulation algorithms for sampling time series with prescribed histograms of patterns and transition probabilities between them. Through this analysis, we better understand the intricacies of ordinal patterns and their statistical attributes.
publishDate 2023
dc.date.none.fl_str_mv 2023-11
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/11336/224578
Rey, Andrea Alejandra; Frery, A. C.; Gambini, J.; Lucini, María Magdalena; The asymptotic distribution of the permutation entropy; American Institute of Physics; Chaos; 33; 11; 11-2023; 1-25
1054-1500
CONICET Digital
CONICET
url http://hdl.handle.net/11336/224578
identifier_str_mv Rey, Andrea Alejandra; Frery, A. C.; Gambini, J.; Lucini, María Magdalena; The asymptotic distribution of the permutation entropy; American Institute of Physics; Chaos; 33; 11; 11-2023; 1-25
1054-1500
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://pubs.aip.org/cha/article/33/11/113108/2919291/The-asymptotic-distribution-of-the-permutation
info:eu-repo/semantics/altIdentifier/doi/10.1063/5.0171508
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
application/pdf
dc.publisher.none.fl_str_mv American Institute of Physics
publisher.none.fl_str_mv American Institute of Physics
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 13.070432

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