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
- Institución
- Pontificia Universidad Católica Argentina
- OAI Identificador
- oai:ucacris:123456789/14707
Ver los metadatos del registro completo
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oai:ucacris:123456789/14707 |
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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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1836638364009234432 |
score |
13.070432 |