Un método para alinear series temporales basado en características de la envolvente como punto de anclaje

Autores
Jarne, Cecilia Gisele; Alcain, Pablo Nicolás
Año de publicación
2019
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In the eld of time series analysis, there is not a unique recipe for studying signal similarities. When having the repetition of a pattern, averaging different signals of the same nature could be complicated. Some times averaging is essential in the analysis of the data. Here we propose a method to align and average segments of time series with similar patterns. For this procedure, a simple implementation based on python code is provided. This analysis was inspired by the study of canary sound syllables, but it is possible to apply it in semi-periodic signals of different nature, not necessarily related to sounds
Fil: Jarne, Cecilia Gisele. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología (UNQ-CONICET). Buenos Aires. Argentina
Fil: Alcain, Pablo Nicolás. Universidad de Buenos Aires. Departamento de Física (UBA-FCEN). Buenos Aires. Argentina
Fuente
An. (Asoc. Fís. Argent., En línea) 2019;03(30):68-71
Materia
SIGNAL ALIGNMENT
TIME SERIES
AVERAGING
PYTHON CODE
OPEN SOURCE
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar
Repositorio
Biblioteca Digital (UBA-FCEN)
Institución
Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
OAI Identificador
afa:afa_v30_n03_p068
Acceder
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oai_identifier_str afa:afa_v30_n03_p068
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repository_id_str 1896
network_name_str Biblioteca Digital (UBA-FCEN)
spelling Un método para alinear series temporales basado en características de la envolvente como punto de anclajeA method to align segments of time series based on envelope features as anchor pointsJarne, Cecilia GiseleAlcain, Pablo NicolásSIGNAL ALIGNMENTTIME SERIESAVERAGINGPYTHON CODEOPEN SOURCEIn the eld of time series analysis, there is not a unique recipe for studying signal similarities. When having the repetition of a pattern, averaging different signals of the same nature could be complicated. Some times averaging is essential in the analysis of the data. Here we propose a method to align and average segments of time series with similar patterns. For this procedure, a simple implementation based on python code is provided. This analysis was inspired by the study of canary sound syllables, but it is possible to apply it in semi-periodic signals of different nature, not necessarily related to soundsFil: Jarne, Cecilia Gisele. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología (UNQ-CONICET). Buenos Aires. ArgentinaFil: Alcain, Pablo Nicolás. Universidad de Buenos Aires. Departamento de Física (UBA-FCEN). Buenos Aires. ArgentinaAsociación Física Argentina2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttps://hdl.handle.net/20.500.12110/afa_v30_n03_p068An. (Asoc. Fís. Argent., En línea) 2019;03(30):68-71reponame:Biblioteca Digital (UBA-FCEN)instname:Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturalesinstacron:UBA-FCENenginfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar2025-09-29T13:40:29Zafa:afa_v30_n03_p068Institucionalhttps://digital.bl.fcen.uba.ar/Universidad públicaNo correspondehttps://digital.bl.fcen.uba.ar/cgi-bin/oaiserver.cgiana@bl.fcen.uba.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:18962025-09-29 13:40:30.313Biblioteca Digital (UBA-FCEN) - Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturalesfalse
dc.title.none.fl_str_mv Un método para alinear series temporales basado en características de la envolvente como punto de anclaje
A method to align segments of time series based on envelope features as anchor points
title Un método para alinear series temporales basado en características de la envolvente como punto de anclaje
spellingShingle Un método para alinear series temporales basado en características de la envolvente como punto de anclaje
Jarne, Cecilia Gisele
SIGNAL ALIGNMENT
TIME SERIES
AVERAGING
PYTHON CODE
OPEN SOURCE
title_short Un método para alinear series temporales basado en características de la envolvente como punto de anclaje
title_full Un método para alinear series temporales basado en características de la envolvente como punto de anclaje
title_fullStr Un método para alinear series temporales basado en características de la envolvente como punto de anclaje
title_full_unstemmed Un método para alinear series temporales basado en características de la envolvente como punto de anclaje
title_sort Un método para alinear series temporales basado en características de la envolvente como punto de anclaje
dc.creator.none.fl_str_mv Jarne, Cecilia Gisele
Alcain, Pablo Nicolás
author Jarne, Cecilia Gisele
author_facet Jarne, Cecilia Gisele
Alcain, Pablo Nicolás
author_role author
author2 Alcain, Pablo Nicolás
author2_role author
dc.subject.none.fl_str_mv SIGNAL ALIGNMENT
TIME SERIES
AVERAGING
PYTHON CODE
OPEN SOURCE
topic SIGNAL ALIGNMENT
TIME SERIES
AVERAGING
PYTHON CODE
OPEN SOURCE
dc.description.none.fl_txt_mv In the eld of time series analysis, there is not a unique recipe for studying signal similarities. When having the repetition of a pattern, averaging different signals of the same nature could be complicated. Some times averaging is essential in the analysis of the data. Here we propose a method to align and average segments of time series with similar patterns. For this procedure, a simple implementation based on python code is provided. This analysis was inspired by the study of canary sound syllables, but it is possible to apply it in semi-periodic signals of different nature, not necessarily related to sounds
Fil: Jarne, Cecilia Gisele. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología (UNQ-CONICET). Buenos Aires. Argentina
Fil: Alcain, Pablo Nicolás. Universidad de Buenos Aires. Departamento de Física (UBA-FCEN). Buenos Aires. Argentina
description In the eld of time series analysis, there is not a unique recipe for studying signal similarities. When having the repetition of a pattern, averaging different signals of the same nature could be complicated. Some times averaging is essential in the analysis of the data. Here we propose a method to align and average segments of time series with similar patterns. For this procedure, a simple implementation based on python code is provided. This analysis was inspired by the study of canary sound syllables, but it is possible to apply it in semi-periodic signals of different nature, not necessarily related to sounds
publishDate 2019
dc.date.none.fl_str_mv 2019
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 https://hdl.handle.net/20.500.12110/afa_v30_n03_p068
url https://hdl.handle.net/20.500.12110/afa_v30_n03_p068
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/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
dc.publisher.none.fl_str_mv Asociación Física Argentina
publisher.none.fl_str_mv Asociación Física Argentina
dc.source.none.fl_str_mv An. (Asoc. Fís. Argent., En línea) 2019;03(30):68-71
reponame:Biblioteca Digital (UBA-FCEN)
instname:Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
instacron:UBA-FCEN
reponame_str Biblioteca Digital (UBA-FCEN)
collection Biblioteca Digital (UBA-FCEN)
instname_str Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
instacron_str UBA-FCEN
institution UBA-FCEN
repository.name.fl_str_mv Biblioteca Digital (UBA-FCEN) - Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
repository.mail.fl_str_mv ana@bl.fcen.uba.ar
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score 13.070432

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