Time Alignment Measurement for Time Series

Detalhes bibliográficos
Autor(a) principal: Folgado, Duarte
Data de Publicação: 2018
Outros Autores: Barandas, Marília, Matias, Ricardo, Martins, Rodrigo S., Carvalho, Miguel, Gamboa, Hugo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
DOI: 10.1016/j.patcog.2018.04.003
Texto Completo: https://doi.org/10.1016/j.patcog.2018.04.003
Resumo: Sem PDF conforme despacho. This work was supported by North Portugal Regional Operational Programme (NORTE 2020), Portugal 2020 and the European Regional Development Fund (ERDF) from European Union through the project Symbiotic technology for societal efficiency gains: Deus ex Machina (DEM) [NORTE-01-0145-FEDER-000026].
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spelling Time Alignment Measurement for Time SeriesDistanceSignal alignmentSimilarityTime seriesTime warpingSoftwareSignal ProcessingComputer Vision and Pattern RecognitionArtificial IntelligenceSem PDF conforme despacho. This work was supported by North Portugal Regional Operational Programme (NORTE 2020), Portugal 2020 and the European Regional Development Fund (ERDF) from European Union through the project Symbiotic technology for societal efficiency gains: Deus ex Machina (DEM) [NORTE-01-0145-FEDER-000026].When a comparison between time series is required, measurement functions provide meaningful scores to characterize similarity between sequences. Quite often, time series appear warped in time, i.e, although they may exhibit amplitude and shape similarity, they appear dephased in time. The most common algorithm to overcome this challenge is the Dynamic Time Warping, which aligns each sequence prior establishing distance measurements. However, Dynamic Time Warping takes only into account amplitude similarity. A distance which characterizes the degree of time warping between two sequences can deliver new insights for applications where the timing factor is essential, such well-defined movements during sports or rehabilitation exercises. We propose a novel measurement called Time Alignment Measurement, which delivers similarity information on the temporal domain. We demonstrate the potential of our approach in measuring performance of time series alignment methodologies and in the characterization of synthetic and real time series data acquired during human movement.DF – Departamento de FísicaLIBPhys-UNLRUNFolgado, DuarteBarandas, MaríliaMatias, RicardoMartins, Rodrigo S.Carvalho, MiguelGamboa, Hugo2019-01-30T23:41:46Z2018-09-012018-09-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article12application/pdfhttps://doi.org/10.1016/j.patcog.2018.04.003eng0031-3203PURE: 11400423http://www.scopus.com/inward/record.url?scp=85045472724&partnerID=8YFLogxKhttps://doi.org/10.1016/j.patcog.2018.04.003info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-05-22T17:36:54Zoai:run.unl.pt:10362/59127Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-05-22T17:36:54Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Time Alignment Measurement for Time Series
title Time Alignment Measurement for Time Series
spellingShingle Time Alignment Measurement for Time Series
Time Alignment Measurement for Time Series
Folgado, Duarte
Distance
Signal alignment
Similarity
Time series
Time warping
Software
Signal Processing
Computer Vision and Pattern Recognition
Artificial Intelligence
Folgado, Duarte
Distance
Signal alignment
Similarity
Time series
Time warping
Software
Signal Processing
Computer Vision and Pattern Recognition
Artificial Intelligence
title_short Time Alignment Measurement for Time Series
title_full Time Alignment Measurement for Time Series
title_fullStr Time Alignment Measurement for Time Series
Time Alignment Measurement for Time Series
title_full_unstemmed Time Alignment Measurement for Time Series
Time Alignment Measurement for Time Series
title_sort Time Alignment Measurement for Time Series
author Folgado, Duarte
author_facet Folgado, Duarte
Folgado, Duarte
Barandas, Marília
Matias, Ricardo
Martins, Rodrigo S.
Carvalho, Miguel
Gamboa, Hugo
Barandas, Marília
Matias, Ricardo
Martins, Rodrigo S.
Carvalho, Miguel
Gamboa, Hugo
author_role author
author2 Barandas, Marília
Matias, Ricardo
Martins, Rodrigo S.
Carvalho, Miguel
Gamboa, Hugo
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv DF – Departamento de Física
LIBPhys-UNL
RUN
dc.contributor.author.fl_str_mv Folgado, Duarte
Barandas, Marília
Matias, Ricardo
Martins, Rodrigo S.
Carvalho, Miguel
Gamboa, Hugo
dc.subject.por.fl_str_mv Distance
Signal alignment
Similarity
Time series
Time warping
Software
Signal Processing
Computer Vision and Pattern Recognition
Artificial Intelligence
topic Distance
Signal alignment
Similarity
Time series
Time warping
Software
Signal Processing
Computer Vision and Pattern Recognition
Artificial Intelligence
description Sem PDF conforme despacho. This work was supported by North Portugal Regional Operational Programme (NORTE 2020), Portugal 2020 and the European Regional Development Fund (ERDF) from European Union through the project Symbiotic technology for societal efficiency gains: Deus ex Machina (DEM) [NORTE-01-0145-FEDER-000026].
publishDate 2018
dc.date.none.fl_str_mv 2018-09-01
2018-09-01T00:00:00Z
2019-01-30T23:41:46Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://doi.org/10.1016/j.patcog.2018.04.003
url https://doi.org/10.1016/j.patcog.2018.04.003
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0031-3203
PURE: 11400423
http://www.scopus.com/inward/record.url?scp=85045472724&partnerID=8YFLogxK
https://doi.org/10.1016/j.patcog.2018.04.003
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 12
application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv mluisa.alvim@gmail.com
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dc.identifier.doi.none.fl_str_mv 10.1016/j.patcog.2018.04.003

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