Comparing different approaches to compute Permutation Entropy with coarse time series

Autores
Traversaro, Francisco; Ciarrocchi, Nicolás; Pollo Cattaneo, Florencia; Redelico, Francisco
Año de publicación
2019
Idioma
inglés
Tipo de recurso
artículo
Estado
versión aceptada
Descripción
Fil: Traversaro, Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Ciarrocchi, Nicolás. Hospital Italiano de Buenos Aires; Argentina
Fil: Traversaro, Francisco. Pontificia Universidad Católica Argentina; Argentina
Fil: Pollo Cattaneo, Florencia. Universidad Tecnológica Nacional; Argentina
Fil: Redelico, Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Redelico, Francisco. Universidad Nacional de Quilmes; Argentina
Abstract: Bandt and Pompe introduced Permutation Entropy as a complexity measure and has been widely used in time series analysis and in many fields of nonlinear dynamics. In theory these time series come from a process that generates continuous values, and if equal values exists in a neighborhood, xt∗ = xt , t∗ ̸= t, they can be neglected with no consequences because their probability of occurrence is insignificant. Since then, this measure has been modified and extended, in particular in cases when the amount of equal values in the time series is large due to the observational method, and cannot be neglected. We test the new Data Driven Method of Imputation that cope with this type of time series without modifying the essence of the Bandt and Pompe Probability Distribution Function and compare it with the Modified Permutation Entropy, a complexity measure that assumes that equal values are not from artifacts of observations but they are typical of the data generator process. The Data Driven Method of Imputation proves to outperform the Modified Permutation Entropy.
Fuente
Physica A: Statistical Mechanics and its Applications. 2019, 513
Materia
SERIES TEMPORALES
ENTROPIA
DINAMICA DE SISTEMAS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
Repositorio Institucional (UCA)
Institución
Pontificia Universidad Católica Argentina
OAI Identificador
oai:ucacris:123456789/14707

id RIUCA_9e8ffd46d66d3fdcc2b1c262937d9caa
oai_identifier_str oai:ucacris:123456789/14707
network_acronym_str RIUCA
repository_id_str 2585
network_name_str Repositorio Institucional (UCA)
spelling Comparing different approaches to compute Permutation Entropy with coarse time seriesTraversaro, FranciscoCiarrocchi, NicolásPollo Cattaneo, FlorenciaRedelico, FranciscoSERIES TEMPORALESENTROPIADINAMICA DE SISTEMASFil: Traversaro, Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Ciarrocchi, Nicolás. Hospital Italiano de Buenos Aires; ArgentinaFil: Traversaro, Francisco. Pontificia Universidad Católica Argentina; ArgentinaFil: Pollo Cattaneo, Florencia. Universidad Tecnológica Nacional; ArgentinaFil: Redelico, Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Redelico, Francisco. Universidad Nacional de Quilmes; ArgentinaAbstract: Bandt and Pompe introduced Permutation Entropy as a complexity measure and has been widely used in time series analysis and in many fields of nonlinear dynamics. In theory these time series come from a process that generates continuous values, and if equal values exists in a neighborhood, xt∗ = xt , t∗ ̸= t, they can be neglected with no consequences because their probability of occurrence is insignificant. Since then, this measure has been modified and extended, in particular in cases when the amount of equal values in the time series is large due to the observational method, and cannot be neglected. We test the new Data Driven Method of Imputation that cope with this type of time series without modifying the essence of the Bandt and Pompe Probability Distribution Function and compare it with the Modified Permutation Entropy, a complexity measure that assumes that equal values are not from artifacts of observations but they are typical of the data generator process. The Data Driven Method of Imputation proves to outperform the Modified Permutation Entropy.Elsevier2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttps://repositorio.uca.edu.ar/handle/123456789/147070378-4371https://doi.org/10.1016/j.physa.2018.08.021Traversaro, F. et al. Comparing different approaches to compute Permutation Entropy with coarse time series [en línea]. En: Physica A: Statistical Mechanics and its Applications. 2019, 513. Disponible en: https://repositorio.uca.edu.ar/handle/123456789/14707Physica A: Statistical Mechanics and its Applications. 2019, 513reponame:Repositorio Institucional (UCA)instname:Pontificia Universidad Católica Argentinaenginfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/4.0/2025-07-03T10:58:45Zoai:ucacris:123456789/14707instacron:UCAInstitucionalhttps://repositorio.uca.edu.ar/Universidad privadaNo correspondehttps://repositorio.uca.edu.ar/oaiclaudia_fernandez@uca.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:25852025-07-03 10:58:45.484Repositorio Institucional (UCA) - Pontificia Universidad Católica Argentinafalse
dc.title.none.fl_str_mv Comparing different approaches to compute Permutation Entropy with coarse time series
title Comparing different approaches to compute Permutation Entropy with coarse time series
spellingShingle Comparing different approaches to compute Permutation Entropy with coarse time series
Traversaro, Francisco
SERIES TEMPORALES
ENTROPIA
DINAMICA DE SISTEMAS
title_short Comparing different approaches to compute Permutation Entropy with coarse time series
title_full Comparing different approaches to compute Permutation Entropy with coarse time series
title_fullStr Comparing different approaches to compute Permutation Entropy with coarse time series
title_full_unstemmed Comparing different approaches to compute Permutation Entropy with coarse time series
title_sort Comparing different approaches to compute Permutation Entropy with coarse time series
dc.creator.none.fl_str_mv Traversaro, Francisco
Ciarrocchi, Nicolás
Pollo Cattaneo, Florencia
Redelico, Francisco
author Traversaro, Francisco
author_facet Traversaro, Francisco
Ciarrocchi, Nicolás
Pollo Cattaneo, Florencia
Redelico, Francisco
author_role author
author2 Ciarrocchi, Nicolás
Pollo Cattaneo, Florencia
Redelico, Francisco
author2_role author
author
author
dc.subject.none.fl_str_mv SERIES TEMPORALES
ENTROPIA
DINAMICA DE SISTEMAS
topic SERIES TEMPORALES
ENTROPIA
DINAMICA DE SISTEMAS
dc.description.none.fl_txt_mv Fil: Traversaro, Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Ciarrocchi, Nicolás. Hospital Italiano de Buenos Aires; Argentina
Fil: Traversaro, Francisco. Pontificia Universidad Católica Argentina; Argentina
Fil: Pollo Cattaneo, Florencia. Universidad Tecnológica Nacional; Argentina
Fil: Redelico, Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Redelico, Francisco. Universidad Nacional de Quilmes; Argentina
Abstract: Bandt and Pompe introduced Permutation Entropy as a complexity measure and has been widely used in time series analysis and in many fields of nonlinear dynamics. In theory these time series come from a process that generates continuous values, and if equal values exists in a neighborhood, xt∗ = xt , t∗ ̸= t, they can be neglected with no consequences because their probability of occurrence is insignificant. Since then, this measure has been modified and extended, in particular in cases when the amount of equal values in the time series is large due to the observational method, and cannot be neglected. We test the new Data Driven Method of Imputation that cope with this type of time series without modifying the essence of the Bandt and Pompe Probability Distribution Function and compare it with the Modified Permutation Entropy, a complexity measure that assumes that equal values are not from artifacts of observations but they are typical of the data generator process. The Data Driven Method of Imputation proves to outperform the Modified Permutation Entropy.
description Fil: Traversaro, Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
publishDate 2019
dc.date.none.fl_str_mv 2019
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://repositorio.uca.edu.ar/handle/123456789/14707
0378-4371
https://doi.org/10.1016/j.physa.2018.08.021
Traversaro, F. et al. Comparing different approaches to compute Permutation Entropy with coarse time series [en línea]. En: Physica A: Statistical Mechanics and its Applications. 2019, 513. Disponible en: https://repositorio.uca.edu.ar/handle/123456789/14707
url https://repositorio.uca.edu.ar/handle/123456789/14707
https://doi.org/10.1016/j.physa.2018.08.021
identifier_str_mv 0378-4371
Traversaro, F. et al. Comparing different approaches to compute Permutation Entropy with coarse time series [en línea]. En: Physica A: Statistical Mechanics and its Applications. 2019, 513. Disponible en: https://repositorio.uca.edu.ar/handle/123456789/14707
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/4.0/
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv Physica A: Statistical Mechanics and its Applications. 2019, 513
reponame:Repositorio Institucional (UCA)
instname:Pontificia Universidad Católica Argentina
reponame_str Repositorio Institucional (UCA)
collection Repositorio Institucional (UCA)
instname_str Pontificia Universidad Católica Argentina
repository.name.fl_str_mv Repositorio Institucional (UCA) - Pontificia Universidad Católica Argentina
repository.mail.fl_str_mv claudia_fernandez@uca.edu.ar
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score 13.070432