Time dependent clustering of time series
Autor(a) principal: | |
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Data de Publicação: | 2007 |
Outros Autores: | , |
Tipo de documento: | Livro |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/10216/66160 |
Resumo: | In this work we consider the problem of clustering time series. Contrary to other works on this topic, our main concern is to let the most important observations, for instance the most recent, have a larger weight on the analysis. This is done by defining similarities measures between two time series, based on Pearson's correlation coefficient, which uses the notion of weighted mean and weighted covariance, where the weights increase monotonically with the time. We use these measures, which are metrics between time series, as a similarity or dissimilarity index between the $n$ time series to be clustered. We apply a very well known partitional method, the K-means, with some adaptations to make it able to choose the number of clusters. |
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Time dependent clustering of time seriesEstatística, MatemáticaStatistics, MathematicsIn this work we consider the problem of clustering time series. Contrary to other works on this topic, our main concern is to let the most important observations, for instance the most recent, have a larger weight on the analysis. This is done by defining similarities measures between two time series, based on Pearson's correlation coefficient, which uses the notion of weighted mean and weighted covariance, where the weights increase monotonically with the time. We use these measures, which are metrics between time series, as a similarity or dissimilarity index between the $n$ time series to be clustered. We apply a very well known partitional method, the K-means, with some adaptations to make it able to choose the number of clusters.20072007-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bookapplication/pdfhttps://hdl.handle.net/10216/66160engJoaquim F. Pinto da CostaIsabel SilvaM. Eduarda Silvainfo: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:RCAAP2023-11-29T15:09:33Zoai:repositorio-aberto.up.pt:10216/66160Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:17:03.030080Repositório 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 dependent clustering of time series |
title |
Time dependent clustering of time series |
spellingShingle |
Time dependent clustering of time series Joaquim F. Pinto da Costa Estatística, Matemática Statistics, Mathematics |
title_short |
Time dependent clustering of time series |
title_full |
Time dependent clustering of time series |
title_fullStr |
Time dependent clustering of time series |
title_full_unstemmed |
Time dependent clustering of time series |
title_sort |
Time dependent clustering of time series |
author |
Joaquim F. Pinto da Costa |
author_facet |
Joaquim F. Pinto da Costa Isabel Silva M. Eduarda Silva |
author_role |
author |
author2 |
Isabel Silva M. Eduarda Silva |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Joaquim F. Pinto da Costa Isabel Silva M. Eduarda Silva |
dc.subject.por.fl_str_mv |
Estatística, Matemática Statistics, Mathematics |
topic |
Estatística, Matemática Statistics, Mathematics |
description |
In this work we consider the problem of clustering time series. Contrary to other works on this topic, our main concern is to let the most important observations, for instance the most recent, have a larger weight on the analysis. This is done by defining similarities measures between two time series, based on Pearson's correlation coefficient, which uses the notion of weighted mean and weighted covariance, where the weights increase monotonically with the time. We use these measures, which are metrics between time series, as a similarity or dissimilarity index between the $n$ time series to be clustered. We apply a very well known partitional method, the K-means, with some adaptations to make it able to choose the number of clusters. |
publishDate |
2007 |
dc.date.none.fl_str_mv |
2007 2007-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/book |
format |
book |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/10216/66160 |
url |
https://hdl.handle.net/10216/66160 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
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 |
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1799136090549911552 |