A simple and fast representation space for classifying complex time series

Autores
Zunino, Luciano José; Olivares Zamora, Felipe Esteban; Bariviera, Aurelio F.; Rosso, Osvaldo Aníbal
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In the context of time series analysis considerable effort has been directed towards the implementation of efficient discriminating statistical quantifiers. Very recently, a simple and fast representation space has been introduced, namely the number of turning points versus the Abbe value. It is able to separate time series from stationary and non-stationary processes with long-range dependences. In this work we show that this bidimensional approach is useful for distinguishing complex time series: different sets of financial and physiological data are efficiently discriminated. Additionally, a multiscale generalization that takes into account the multiple time scales often involved in complex systems has been also proposed. This multiscale analysis is essential to reach a higher discriminative power between physiological time series in health and disease.
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. Universidad Nacional de La Plata. Facultad de Ingeniería; Argentina
Fil: Olivares Zamora, Felipe Esteban. Pontificia Universidad Católica de Valparaíso; Chile
Fil: Bariviera, Aurelio F.. Universitat Rovira I Virgili; España
Fil: Rosso, Osvaldo Aníbal. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidade Federal de Alagoas; Brasil. Instituto Tecnológico de Buenos Aires; Argentina. Universidad de los Andes; Chile
Materia
Time Series Analysis
Abbe Value
Turning Points
Financial Data
Electroencephalogram Data
Heart Rate Variability
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:11336/40735
Acceder
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network_name_str CONICET Digital (CONICET)
spelling A simple and fast representation space for classifying complex time seriesZunino, Luciano JoséOlivares Zamora, Felipe EstebanBariviera, Aurelio F.Rosso, Osvaldo AníbalTime Series AnalysisAbbe ValueTurning PointsFinancial DataElectroencephalogram DataHeart Rate Variabilityhttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1In the context of time series analysis considerable effort has been directed towards the implementation of efficient discriminating statistical quantifiers. Very recently, a simple and fast representation space has been introduced, namely the number of turning points versus the Abbe value. It is able to separate time series from stationary and non-stationary processes with long-range dependences. In this work we show that this bidimensional approach is useful for distinguishing complex time series: different sets of financial and physiological data are efficiently discriminated. Additionally, a multiscale generalization that takes into account the multiple time scales often involved in complex systems has been also proposed. This multiscale analysis is essential to reach a higher discriminative power between physiological time series in health and disease.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. Universidad Nacional de La Plata. Facultad de Ingeniería; ArgentinaFil: Olivares Zamora, Felipe Esteban. Pontificia Universidad Católica de Valparaíso; ChileFil: Bariviera, Aurelio F.. Universitat Rovira I Virgili; EspañaFil: Rosso, Osvaldo Aníbal. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidade Federal de Alagoas; Brasil. Instituto Tecnológico de Buenos Aires; Argentina. Universidad de los Andes; ChileElsevier Science2017-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/40735Zunino, Luciano José; Olivares Zamora, Felipe Esteban; Bariviera, Aurelio F.; Rosso, Osvaldo Aníbal; A simple and fast representation space for classifying complex time series; Elsevier Science; Physics Letters A; 381; 11; 1-2017; 1021-10280375-9601CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.physleta.2017.01.047info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0375960116316681info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:48:06Zoai:ri.conicet.gov.ar:11336/40735instacron: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 09:48:06.261CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A simple and fast representation space for classifying complex time series
title A simple and fast representation space for classifying complex time series
spellingShingle A simple and fast representation space for classifying complex time series
Zunino, Luciano José
Time Series Analysis
Abbe Value
Turning Points
Financial Data
Electroencephalogram Data
Heart Rate Variability
title_short A simple and fast representation space for classifying complex time series
title_full A simple and fast representation space for classifying complex time series
title_fullStr A simple and fast representation space for classifying complex time series
title_full_unstemmed A simple and fast representation space for classifying complex time series
title_sort A simple and fast representation space for classifying complex time series
dc.creator.none.fl_str_mv Zunino, Luciano José
Olivares Zamora, Felipe Esteban
Bariviera, Aurelio F.
Rosso, Osvaldo Aníbal
author Zunino, Luciano José
author_facet Zunino, Luciano José
Olivares Zamora, Felipe Esteban
Bariviera, Aurelio F.
Rosso, Osvaldo Aníbal
author_role author
author2 Olivares Zamora, Felipe Esteban
Bariviera, Aurelio F.
Rosso, Osvaldo Aníbal
author2_role author
author
author
dc.subject.none.fl_str_mv Time Series Analysis
Abbe Value
Turning Points
Financial Data
Electroencephalogram Data
Heart Rate Variability
topic Time Series Analysis
Abbe Value
Turning Points
Financial Data
Electroencephalogram Data
Heart Rate Variability
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 the context of time series analysis considerable effort has been directed towards the implementation of efficient discriminating statistical quantifiers. Very recently, a simple and fast representation space has been introduced, namely the number of turning points versus the Abbe value. It is able to separate time series from stationary and non-stationary processes with long-range dependences. In this work we show that this bidimensional approach is useful for distinguishing complex time series: different sets of financial and physiological data are efficiently discriminated. Additionally, a multiscale generalization that takes into account the multiple time scales often involved in complex systems has been also proposed. This multiscale analysis is essential to reach a higher discriminative power between physiological time series in health and disease.
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. Universidad Nacional de La Plata. Facultad de Ingeniería; Argentina
Fil: Olivares Zamora, Felipe Esteban. Pontificia Universidad Católica de Valparaíso; Chile
Fil: Bariviera, Aurelio F.. Universitat Rovira I Virgili; España
Fil: Rosso, Osvaldo Aníbal. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidade Federal de Alagoas; Brasil. Instituto Tecnológico de Buenos Aires; Argentina. Universidad de los Andes; Chile
description In the context of time series analysis considerable effort has been directed towards the implementation of efficient discriminating statistical quantifiers. Very recently, a simple and fast representation space has been introduced, namely the number of turning points versus the Abbe value. It is able to separate time series from stationary and non-stationary processes with long-range dependences. In this work we show that this bidimensional approach is useful for distinguishing complex time series: different sets of financial and physiological data are efficiently discriminated. Additionally, a multiscale generalization that takes into account the multiple time scales often involved in complex systems has been also proposed. This multiscale analysis is essential to reach a higher discriminative power between physiological time series in health and disease.
publishDate 2017
dc.date.none.fl_str_mv 2017-01
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/11336/40735
Zunino, Luciano José; Olivares Zamora, Felipe Esteban; Bariviera, Aurelio F.; Rosso, Osvaldo Aníbal; A simple and fast representation space for classifying complex time series; Elsevier Science; Physics Letters A; 381; 11; 1-2017; 1021-1028
0375-9601
CONICET Digital
CONICET
url http://hdl.handle.net/11336/40735
identifier_str_mv Zunino, Luciano José; Olivares Zamora, Felipe Esteban; Bariviera, Aurelio F.; Rosso, Osvaldo Aníbal; A simple and fast representation space for classifying complex time series; Elsevier Science; Physics Letters A; 381; 11; 1-2017; 1021-1028
0375-9601
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.physleta.2017.01.047
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0375960116316681
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
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
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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