20 years of ordinal patterns: Perspectives and challenges

Autores
Leyva, Inmaculada; Martínez, Johann H.; Masoller, Cristina; Rosso, Osvaldo Anibal; Zanin, Massimiliano
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In 2002, in a seminal article, Bandt and Pompe proposed a new methodology for the analysis of complex time series, now known as Ordinal Analysis. The ordinal methodology is based on the computation of symbols (known as ordinal patters) which are defined in terms of the temporal ordering of data points in a time series, and whose probabilities are known as ordinal probabilities. With the ordinal probabilities the Shannon entropy can be calculated, which is the permutation entropy. Since it was proposed, the ordinal method has found applications in fields as diverse as biomedicine and climatology. However, some properties of ordinal probabilities are still not fully understood, and how to combine the ordinal approach of feature extraction with machine learning techniques for model identification, time series classification or forecasting, remains a challenge. The objective of this perspective article is to present some recent advances and to discuss some open problems.
Fil: Leyva, Inmaculada. Universidad Rey Juan Carlos; España. Universidad Politécnica de Madrid; España
Fil: Martínez, Johann H.. Consejo Superior de Investigaciones Científicas. Instituto de Física Interdisciplinar y Sistemas Complejos; España
Fil: Masoller, Cristina. Universidad Politécnica de Catalunya; España
Fil: Rosso, Osvaldo Anibal. Universidade Federal de Alagoas; Brasil. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Zanin, Massimiliano. Consejo Superior de Investigaciones Científicas. Instituto de Física Interdisciplinar y Sistemas Complejos; España
Materia
biomedicine
climatology
feature extraction
open problems
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:11336/213573
Acceder
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network_name_str CONICET Digital (CONICET)
spelling 20 years of ordinal patterns: Perspectives and challengesLeyva, InmaculadaMartínez, Johann H.Masoller, CristinaRosso, Osvaldo AnibalZanin, Massimilianobiomedicineclimatologyfeature extractionopen problemshttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1In 2002, in a seminal article, Bandt and Pompe proposed a new methodology for the analysis of complex time series, now known as Ordinal Analysis. The ordinal methodology is based on the computation of symbols (known as ordinal patters) which are defined in terms of the temporal ordering of data points in a time series, and whose probabilities are known as ordinal probabilities. With the ordinal probabilities the Shannon entropy can be calculated, which is the permutation entropy. Since it was proposed, the ordinal method has found applications in fields as diverse as biomedicine and climatology. However, some properties of ordinal probabilities are still not fully understood, and how to combine the ordinal approach of feature extraction with machine learning techniques for model identification, time series classification or forecasting, remains a challenge. The objective of this perspective article is to present some recent advances and to discuss some open problems.Fil: Leyva, Inmaculada. Universidad Rey Juan Carlos; España. Universidad Politécnica de Madrid; EspañaFil: Martínez, Johann H.. Consejo Superior de Investigaciones Científicas. Instituto de Física Interdisciplinar y Sistemas Complejos; EspañaFil: Masoller, Cristina. Universidad Politécnica de Catalunya; EspañaFil: Rosso, Osvaldo Anibal. Universidade Federal de Alagoas; Brasil. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Zanin, Massimiliano. Consejo Superior de Investigaciones Científicas. Instituto de Física Interdisciplinar y Sistemas Complejos; EspañaEurophysics Letters2022-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/213573Leyva, Inmaculada; Martínez, Johann H.; Masoller, Cristina; Rosso, Osvaldo Anibal; Zanin, Massimiliano; 20 years of ordinal patterns: Perspectives and challenges; Europhysics Letters; Europhysics Letters; 138; 3; 5-2022; 1-80295-5075CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1209/0295-5075/ac6a72info:eu-repo/semantics/altIdentifier/url/https://iopscience.iop.org/article/10.1209/0295-5075/ac6a72info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:29:56Zoai:ri.conicet.gov.ar:11336/213573instacron: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 10:29:56.523CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv 20 years of ordinal patterns: Perspectives and challenges
title 20 years of ordinal patterns: Perspectives and challenges
spellingShingle 20 years of ordinal patterns: Perspectives and challenges
Leyva, Inmaculada
biomedicine
climatology
feature extraction
open problems
title_short 20 years of ordinal patterns: Perspectives and challenges
title_full 20 years of ordinal patterns: Perspectives and challenges
title_fullStr 20 years of ordinal patterns: Perspectives and challenges
title_full_unstemmed 20 years of ordinal patterns: Perspectives and challenges
title_sort 20 years of ordinal patterns: Perspectives and challenges
dc.creator.none.fl_str_mv Leyva, Inmaculada
Martínez, Johann H.
Masoller, Cristina
Rosso, Osvaldo Anibal
Zanin, Massimiliano
author Leyva, Inmaculada
author_facet Leyva, Inmaculada
Martínez, Johann H.
Masoller, Cristina
Rosso, Osvaldo Anibal
Zanin, Massimiliano
author_role author
author2 Martínez, Johann H.
Masoller, Cristina
Rosso, Osvaldo Anibal
Zanin, Massimiliano
author2_role author
author
author
author
dc.subject.none.fl_str_mv biomedicine
climatology
feature extraction
open problems
topic biomedicine
climatology
feature extraction
open problems
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv In 2002, in a seminal article, Bandt and Pompe proposed a new methodology for the analysis of complex time series, now known as Ordinal Analysis. The ordinal methodology is based on the computation of symbols (known as ordinal patters) which are defined in terms of the temporal ordering of data points in a time series, and whose probabilities are known as ordinal probabilities. With the ordinal probabilities the Shannon entropy can be calculated, which is the permutation entropy. Since it was proposed, the ordinal method has found applications in fields as diverse as biomedicine and climatology. However, some properties of ordinal probabilities are still not fully understood, and how to combine the ordinal approach of feature extraction with machine learning techniques for model identification, time series classification or forecasting, remains a challenge. The objective of this perspective article is to present some recent advances and to discuss some open problems.
Fil: Leyva, Inmaculada. Universidad Rey Juan Carlos; España. Universidad Politécnica de Madrid; España
Fil: Martínez, Johann H.. Consejo Superior de Investigaciones Científicas. Instituto de Física Interdisciplinar y Sistemas Complejos; España
Fil: Masoller, Cristina. Universidad Politécnica de Catalunya; España
Fil: Rosso, Osvaldo Anibal. Universidade Federal de Alagoas; Brasil. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Zanin, Massimiliano. Consejo Superior de Investigaciones Científicas. Instituto de Física Interdisciplinar y Sistemas Complejos; España
description In 2002, in a seminal article, Bandt and Pompe proposed a new methodology for the analysis of complex time series, now known as Ordinal Analysis. The ordinal methodology is based on the computation of symbols (known as ordinal patters) which are defined in terms of the temporal ordering of data points in a time series, and whose probabilities are known as ordinal probabilities. With the ordinal probabilities the Shannon entropy can be calculated, which is the permutation entropy. Since it was proposed, the ordinal method has found applications in fields as diverse as biomedicine and climatology. However, some properties of ordinal probabilities are still not fully understood, and how to combine the ordinal approach of feature extraction with machine learning techniques for model identification, time series classification or forecasting, remains a challenge. The objective of this perspective article is to present some recent advances and to discuss some open problems.
publishDate 2022
dc.date.none.fl_str_mv 2022-05
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/11336/213573
Leyva, Inmaculada; Martínez, Johann H.; Masoller, Cristina; Rosso, Osvaldo Anibal; Zanin, Massimiliano; 20 years of ordinal patterns: Perspectives and challenges; Europhysics Letters; Europhysics Letters; 138; 3; 5-2022; 1-8
0295-5075
CONICET Digital
CONICET
url http://hdl.handle.net/11336/213573
identifier_str_mv Leyva, Inmaculada; Martínez, Johann H.; Masoller, Cristina; Rosso, Osvaldo Anibal; Zanin, Massimiliano; 20 years of ordinal patterns: Perspectives and challenges; Europhysics Letters; Europhysics Letters; 138; 3; 5-2022; 1-8
0295-5075
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1209/0295-5075/ac6a72
info:eu-repo/semantics/altIdentifier/url/https://iopscience.iop.org/article/10.1209/0295-5075/ac6a72
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
dc.publisher.none.fl_str_mv Europhysics Letters
publisher.none.fl_str_mv Europhysics Letters
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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