Using missing ordinal patterns to detect nonlinearity in time series data

Autores
Kulp, Christopher W.; Zunino, Luciano José; Osborne, Thomas; Zawadzki, Brianna
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The number of missing ordinal patterns (NMP) is the number of ordinal patterns that do not appear in a series after it has been symbolized using the Bandt and Pompe methodology. In this paper, the NMP is demonstrated as a test for nonlinearity using a surrogate framework in order to see if the NMP for a series is statistically different from the NMP of iterative amplitude adjusted Fourier transform (IAAFT) surrogates. It is found that the NMP works well as a test statistic for nonlinearity, even in the cases of very short time series. Both model and experimental time series are used to demonstrate the efficacy of the NMP as a test for nonlinearity.
Fil: Kulp, Christopher W.. Lycoming College; Estados Unidos
Fil: Zunino, Luciano José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigaciones Ópticas. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones Ópticas. Universidad Nacional de La Plata. Centro de Investigaciones Ópticas; Argentina
Fil: Osborne, Thomas. Lycoming College; Estados Unidos
Fil: Zawadzki, Brianna. Lycoming College; Estados Unidos
Materia
Time Series Analysis
Nonlinearity
Missing Ordinal Patterns
Surrogate Method
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:11336/49242
Acceder
id CONICETDig_145f8984e53a0cbe394b20b1cecf1563
oai_identifier_str oai:ri.conicet.gov.ar:11336/49242
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Using missing ordinal patterns to detect nonlinearity in time series dataKulp, Christopher W.Zunino, Luciano JoséOsborne, ThomasZawadzki, BriannaTime Series AnalysisNonlinearityMissing Ordinal PatternsSurrogate Methodhttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1The number of missing ordinal patterns (NMP) is the number of ordinal patterns that do not appear in a series after it has been symbolized using the Bandt and Pompe methodology. In this paper, the NMP is demonstrated as a test for nonlinearity using a surrogate framework in order to see if the NMP for a series is statistically different from the NMP of iterative amplitude adjusted Fourier transform (IAAFT) surrogates. It is found that the NMP works well as a test statistic for nonlinearity, even in the cases of very short time series. Both model and experimental time series are used to demonstrate the efficacy of the NMP as a test for nonlinearity.Fil: Kulp, Christopher W.. Lycoming College; Estados UnidosFil: Zunino, Luciano José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigaciones Ópticas. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones Ópticas. Universidad Nacional de La Plata. Centro de Investigaciones Ópticas; ArgentinaFil: Osborne, Thomas. Lycoming College; Estados UnidosFil: Zawadzki, Brianna. Lycoming College; Estados UnidosAmerican Physical Society2017-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/49242Kulp, Christopher W.; Zunino, Luciano José; Osborne, Thomas; Zawadzki, Brianna; Using missing ordinal patterns to detect nonlinearity in time series data; American Physical Society; Physical Review E; 96; 2; 8-2017; 1-10; 0222182470-0053CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1103/PhysRevE.96.022218info:eu-repo/semantics/altIdentifier/url/https://journals.aps.org/pre/abstract/10.1103/PhysRevE.96.022218info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-11-12T09:49:29Zoai:ri.conicet.gov.ar:11336/49242instacron: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:34982025-11-12 09:49:29.721CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Using missing ordinal patterns to detect nonlinearity in time series data
title Using missing ordinal patterns to detect nonlinearity in time series data
spellingShingle Using missing ordinal patterns to detect nonlinearity in time series data
Kulp, Christopher W.
Time Series Analysis
Nonlinearity
Missing Ordinal Patterns
Surrogate Method
title_short Using missing ordinal patterns to detect nonlinearity in time series data
title_full Using missing ordinal patterns to detect nonlinearity in time series data
title_fullStr Using missing ordinal patterns to detect nonlinearity in time series data
title_full_unstemmed Using missing ordinal patterns to detect nonlinearity in time series data
title_sort Using missing ordinal patterns to detect nonlinearity in time series data
dc.creator.none.fl_str_mv Kulp, Christopher W.
Zunino, Luciano José
Osborne, Thomas
Zawadzki, Brianna
author Kulp, Christopher W.
author_facet Kulp, Christopher W.
Zunino, Luciano José
Osborne, Thomas
Zawadzki, Brianna
author_role author
author2 Zunino, Luciano José
Osborne, Thomas
Zawadzki, Brianna
author2_role author
author
author
dc.subject.none.fl_str_mv Time Series Analysis
Nonlinearity
Missing Ordinal Patterns
Surrogate Method
topic Time Series Analysis
Nonlinearity
Missing Ordinal Patterns
Surrogate Method
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The number of missing ordinal patterns (NMP) is the number of ordinal patterns that do not appear in a series after it has been symbolized using the Bandt and Pompe methodology. In this paper, the NMP is demonstrated as a test for nonlinearity using a surrogate framework in order to see if the NMP for a series is statistically different from the NMP of iterative amplitude adjusted Fourier transform (IAAFT) surrogates. It is found that the NMP works well as a test statistic for nonlinearity, even in the cases of very short time series. Both model and experimental time series are used to demonstrate the efficacy of the NMP as a test for nonlinearity.
Fil: Kulp, Christopher W.. Lycoming College; Estados Unidos
Fil: Zunino, Luciano José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigaciones Ópticas. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones Ópticas. Universidad Nacional de La Plata. Centro de Investigaciones Ópticas; Argentina
Fil: Osborne, Thomas. Lycoming College; Estados Unidos
Fil: Zawadzki, Brianna. Lycoming College; Estados Unidos
description The number of missing ordinal patterns (NMP) is the number of ordinal patterns that do not appear in a series after it has been symbolized using the Bandt and Pompe methodology. In this paper, the NMP is demonstrated as a test for nonlinearity using a surrogate framework in order to see if the NMP for a series is statistically different from the NMP of iterative amplitude adjusted Fourier transform (IAAFT) surrogates. It is found that the NMP works well as a test statistic for nonlinearity, even in the cases of very short time series. Both model and experimental time series are used to demonstrate the efficacy of the NMP as a test for nonlinearity.
publishDate 2017
dc.date.none.fl_str_mv 2017-08
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/11336/49242
Kulp, Christopher W.; Zunino, Luciano José; Osborne, Thomas; Zawadzki, Brianna; Using missing ordinal patterns to detect nonlinearity in time series data; American Physical Society; Physical Review E; 96; 2; 8-2017; 1-10; 022218
2470-0053
CONICET Digital
CONICET
url http://hdl.handle.net/11336/49242
identifier_str_mv Kulp, Christopher W.; Zunino, Luciano José; Osborne, Thomas; Zawadzki, Brianna; Using missing ordinal patterns to detect nonlinearity in time series data; American Physical Society; Physical Review E; 96; 2; 8-2017; 1-10; 022218
2470-0053
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1103/PhysRevE.96.022218
info:eu-repo/semantics/altIdentifier/url/https://journals.aps.org/pre/abstract/10.1103/PhysRevE.96.022218
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 American Physical Society
publisher.none.fl_str_mv American Physical Society
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
_version_ 1848598074720518144
score 12.976206

Publicaciones similares