A fragmented-periodogram approach for clustering big data time series
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.5/27635 |
Resumo: | We propose and study a new frequency-domain procedure for characterizing and comparing large sets of long time series. Instead of using all the information available from data, which would be computationally very expensive, we propose some regularization rules in order to select and summarize the most relevant information for clustering purposes. Essentially, we suggest to use a fragmented periodogram computed around the driving cyclical components of interest and to compare the various estimates. This procedure is computationally simple, but able to condense relevant information of the time series. A simulation exercise shows that the smoothed fragmented periodogram works in general better than the non-smoothed one and not worse than the complete periodogram for medium to large sample sizes. We illustrate this procedure in a study of the evolution of several stock markets indices. We further show the effect of recent financial crises over these indices behaviour. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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A fragmented-periodogram approach for clustering big data time seriesBig DataFragmented PeriodogramSpectral ClusteringSmoothed PeriodogramTime Series ClusteringWe propose and study a new frequency-domain procedure for characterizing and comparing large sets of long time series. Instead of using all the information available from data, which would be computationally very expensive, we propose some regularization rules in order to select and summarize the most relevant information for clustering purposes. Essentially, we suggest to use a fragmented periodogram computed around the driving cyclical components of interest and to compare the various estimates. This procedure is computationally simple, but able to condense relevant information of the time series. A simulation exercise shows that the smoothed fragmented periodogram works in general better than the non-smoothed one and not worse than the complete periodogram for medium to large sample sizes. We illustrate this procedure in a study of the evolution of several stock markets indices. We further show the effect of recent financial crises over these indices behaviour.SpringerRepositório da Universidade de LisboaCaiado, JorgeCrato, NunoPoncela, Pilar2023-04-17T11:26:54Z20202020-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.5/27635engCaiado, Jorge, Nuno Crato and Pilar Poncela .(2020). “A fragmented-periodogram approach for clustering big data time series”. Advances in Data Analysis and Classification, Vol. 14: pp. 117–146. (Search PDF in 2023).10.1007/s11634-019-00365-8info: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:RCAAP2023-04-23T01:30:49Zoai:www.repository.utl.pt:10400.5/27635Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:50:08.555134Repositório 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 |
A fragmented-periodogram approach for clustering big data time series |
title |
A fragmented-periodogram approach for clustering big data time series |
spellingShingle |
A fragmented-periodogram approach for clustering big data time series Caiado, Jorge Big Data Fragmented Periodogram Spectral Clustering Smoothed Periodogram Time Series Clustering |
title_short |
A fragmented-periodogram approach for clustering big data time series |
title_full |
A fragmented-periodogram approach for clustering big data time series |
title_fullStr |
A fragmented-periodogram approach for clustering big data time series |
title_full_unstemmed |
A fragmented-periodogram approach for clustering big data time series |
title_sort |
A fragmented-periodogram approach for clustering big data time series |
author |
Caiado, Jorge |
author_facet |
Caiado, Jorge Crato, Nuno Poncela, Pilar |
author_role |
author |
author2 |
Crato, Nuno Poncela, Pilar |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Repositório da Universidade de Lisboa |
dc.contributor.author.fl_str_mv |
Caiado, Jorge Crato, Nuno Poncela, Pilar |
dc.subject.por.fl_str_mv |
Big Data Fragmented Periodogram Spectral Clustering Smoothed Periodogram Time Series Clustering |
topic |
Big Data Fragmented Periodogram Spectral Clustering Smoothed Periodogram Time Series Clustering |
description |
We propose and study a new frequency-domain procedure for characterizing and comparing large sets of long time series. Instead of using all the information available from data, which would be computationally very expensive, we propose some regularization rules in order to select and summarize the most relevant information for clustering purposes. Essentially, we suggest to use a fragmented periodogram computed around the driving cyclical components of interest and to compare the various estimates. This procedure is computationally simple, but able to condense relevant information of the time series. A simulation exercise shows that the smoothed fragmented periodogram works in general better than the non-smoothed one and not worse than the complete periodogram for medium to large sample sizes. We illustrate this procedure in a study of the evolution of several stock markets indices. We further show the effect of recent financial crises over these indices behaviour. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020 2020-01-01T00:00:00Z 2023-04-17T11:26:54Z |
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/10400.5/27635 |
url |
http://hdl.handle.net/10400.5/27635 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Caiado, Jorge, Nuno Crato and Pilar Poncela .(2020). “A fragmented-periodogram approach for clustering big data time series”. Advances in Data Analysis and Classification, Vol. 14: pp. 117–146. (Search PDF in 2023). 10.1007/s11634-019-00365-8 |
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.publisher.none.fl_str_mv |
Springer |
publisher.none.fl_str_mv |
Springer |
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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1799131581141483520 |