TSSEARCH

Detalhes bibliográficos
Autor(a) principal: Folgado, Duarte
Data de Publicação: 2022
Outros Autores: Barandas, Marília, Antunes, Margarida, Nunes, Maria Lua, Liu, Hui, Hartmann, Yale, Schultz, Tanja, Gamboa, Hugo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/143322
Resumo: Subsequence search and distance measures are crucial tools in time series data mining. This paper presents our Python package entitled TSSEARCH, which provides a comprehensive set of methods for subsequence search and similarity measurement in time series. These methods are user-customizable for more flexibility and efficient integration into real deployment scenarios. TSSEARCH enables fast exploratory time series data analysis and was validated in the context of human activity recognition and indoor localization.
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spelling TSSEARCHTime Series Subsequence Search LibraryTime seriesSubsequence searchDistancesSimilarity measurementsQuery-based searchSegmentationPython packageSubsequence search and distance measures are crucial tools in time series data mining. This paper presents our Python package entitled TSSEARCH, which provides a comprehensive set of methods for subsequence search and similarity measurement in time series. These methods are user-customizable for more flexibility and efficient integration into real deployment scenarios. TSSEARCH enables fast exploratory time series data analysis and was validated in the context of human activity recognition and indoor localization.LIBPhys-UNLRUNFolgado, DuarteBarandas, MaríliaAntunes, MargaridaNunes, Maria LuaLiu, HuiHartmann, YaleSchultz, TanjaGamboa, Hugo2022-08-26T22:17:22Z2022-062022-06-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article5application/pdfhttp://hdl.handle.net/10362/143322eng2352-7110PURE: 45154650https://doi.org/10.1016/j.softx.2022.101049info: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:RCAAP2024-05-22T18:04:43Zoai:run.unl.pt:10362/143322Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-05-22T18:04:43Repositório 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 TSSEARCH
Time Series Subsequence Search Library
title TSSEARCH
spellingShingle TSSEARCH
Folgado, Duarte
Time series
Subsequence search
Distances
Similarity measurements
Query-based search
Segmentation
Python package
title_short TSSEARCH
title_full TSSEARCH
title_fullStr TSSEARCH
title_full_unstemmed TSSEARCH
title_sort TSSEARCH
author Folgado, Duarte
author_facet Folgado, Duarte
Barandas, Marília
Antunes, Margarida
Nunes, Maria Lua
Liu, Hui
Hartmann, Yale
Schultz, Tanja
Gamboa, Hugo
author_role author
author2 Barandas, Marília
Antunes, Margarida
Nunes, Maria Lua
Liu, Hui
Hartmann, Yale
Schultz, Tanja
Gamboa, Hugo
author2_role author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv LIBPhys-UNL
RUN
dc.contributor.author.fl_str_mv Folgado, Duarte
Barandas, Marília
Antunes, Margarida
Nunes, Maria Lua
Liu, Hui
Hartmann, Yale
Schultz, Tanja
Gamboa, Hugo
dc.subject.por.fl_str_mv Time series
Subsequence search
Distances
Similarity measurements
Query-based search
Segmentation
Python package
topic Time series
Subsequence search
Distances
Similarity measurements
Query-based search
Segmentation
Python package
description Subsequence search and distance measures are crucial tools in time series data mining. This paper presents our Python package entitled TSSEARCH, which provides a comprehensive set of methods for subsequence search and similarity measurement in time series. These methods are user-customizable for more flexibility and efficient integration into real deployment scenarios. TSSEARCH enables fast exploratory time series data analysis and was validated in the context of human activity recognition and indoor localization.
publishDate 2022
dc.date.none.fl_str_mv 2022-08-26T22:17:22Z
2022-06
2022-06-01T00:00:00Z
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 http://hdl.handle.net/10362/143322
url http://hdl.handle.net/10362/143322
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2352-7110
PURE: 45154650
https://doi.org/10.1016/j.softx.2022.101049
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 5
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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