Comparing different approaches to compute Permutation Entropy with coarse time series

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 neighborhoo...

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Detalles Bibliográficos
Autores principales: Traversaro, Francisco, Ciarrocchi, Nicolás, Pollo Cattaneo, Florencia, Redelico, Francisco
Formato: Artículo
Lenguaje:Inglés
Publicado: Elsevier 2022
Materias:
SERIES TEMPORALES
ENTROPIA
DINAMICA DE SISTEMAS
Acceso en línea:https://repositorio.uca.edu.ar/handle/123456789/14707
https://doi.org/10.1016/j.physa.2018.08.021
Aporte de:
Repositorio Institucional de la Universidad Católica Argentina (UCA) de Universidad Católica Argentina
id I33-R139-123456789-14707
record_format dspace
institution Universidad Católica Argentina
institution_str I-33
repository_str R-139
collection Repositorio Institucional de la Universidad Católica Argentina (UCA)
language Inglés
topic SERIES TEMPORALES
ENTROPIA
DINAMICA DE SISTEMAS
spellingShingle SERIES TEMPORALES
ENTROPIA
DINAMICA DE SISTEMAS
Traversaro, Francisco
Ciarrocchi, Nicolás
Pollo Cattaneo, Florencia
Redelico, Francisco
Comparing different approaches to compute Permutation Entropy with coarse time series
topic_facet SERIES TEMPORALES
ENTROPIA
DINAMICA DE SISTEMAS
description 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.
format Artículo
author Traversaro, Francisco
Ciarrocchi, Nicolás
Pollo Cattaneo, Florencia
Redelico, Francisco
author_facet Traversaro, Francisco
Ciarrocchi, Nicolás
Pollo Cattaneo, Florencia
Redelico, Francisco
author_sort Traversaro, Francisco
title Comparing different approaches to compute Permutation Entropy with coarse time series
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
publisher Elsevier
publishDate 2022
url https://repositorio.uca.edu.ar/handle/123456789/14707
https://doi.org/10.1016/j.physa.2018.08.021
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AT ciarrocchinicolas comparingdifferentapproachestocomputepermutationentropywithcoarsetimeseries
AT pollocattaneoflorencia comparingdifferentapproachestocomputepermutationentropywithcoarsetimeseries
AT redelicofrancisco comparingdifferentapproachestocomputepermutationentropywithcoarsetimeseries
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