Publicação
A periodogram-based metric for time series classification
| Resumo: | The statistical discrimination and clustering literature has studied the problem of identifying similarities in time series data. Some studies use non-parametric approaches for splitting a set of time series into clusters by looking at their Euclidean distances in the space of points. A new measure of distance between time series based on the normalized periodogram is proposed. Simulation results comparing this measure with others parametric and non-parametric metrics are provided. In particular, the classification of time series as stationary or as non-stationary is discussed. The use of both hierarchical and non-hierarchical clustering algorithms is considered. An illustrative example with economic time series data is also presented. |
|---|---|
| Autores principais: | Caiado, Jorge |
| Outros Autores: | Crato, Nuno; Peña, Daniel |
| Assunto: | Autocorrelation Function Classification Clustering Euclidean Distance Periodogram Stationary and Non-stationary Time Series |
| Ano: | 2006 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório da Universidade de Lisboa |
| _version_ | 1866810233512263680 |
|---|---|
| author | Caiado, Jorge |
| author2 | Crato, Nuno Peña, Daniel |
| author2_role | author author |
| author_facet | Caiado, Jorge Crato, Nuno Peña, Daniel |
| author_role | author |
| contributor_name_str_mv | Repositório Científico de Acesso Aberto da ULisboa |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Caiado, Jorge\"},{\"Person.name\":\"Crato, Nuno\"},{\"Person.name\":\"Peña, Daniel\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Repositório Científico de Acesso Aberto da ULisboa |
| datacite.creators.creator.creatorName.fl_str_mv | Caiado, Jorge Crato, Nuno Peña, Daniel |
| datacite.date.Accepted.fl_str_mv | 2006-01-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2023-04-28T16:04:17Z |
| datacite.date.embargoed.fl_str_mv | 2023-04-28T16:04:17Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Autocorrelation Function Classification Clustering Euclidean Distance Periodogram Stationary and Non-stationary Time Series |
| datacite.titles.title.fl_str_mv | A periodogram-based metric for time series classification |
| dc.contributor.none.fl_str_mv | Repositório Científico de Acesso Aberto da ULisboa |
| dc.creator.none.fl_str_mv | Caiado, Jorge Crato, Nuno Peña, Daniel |
| dc.date.Accepted.fl_str_mv | 2006-01-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2023-04-28T16:04:17Z |
| dc.date.embargoed.fl_str_mv | 2023-04-28T16:04:17Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10400.5/27675 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | Elsevier |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | Autocorrelation Function Classification Clustering Euclidean Distance Periodogram Stationary and Non-stationary Time Series |
| dc.title.fl_str_mv | A periodogram-based metric for time series classification |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_6501 |
| description | The statistical discrimination and clustering literature has studied the problem of identifying similarities in time series data. Some studies use non-parametric approaches for splitting a set of time series into clusters by looking at their Euclidean distances in the space of points. A new measure of distance between time series based on the normalized periodogram is proposed. Simulation results comparing this measure with others parametric and non-parametric metrics are provided. In particular, the classification of time series as stationary or as non-stationary is discussed. The use of both hierarchical and non-hierarchical clustering algorithms is considered. An illustrative example with economic time series data is also presented. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | article |
| fulltext.url.fl_str_mv | https://repositorio.ulisboa.pt/bitstreams/3719622b-803c-4fbb-8a93-416b527b380a/download |
| id | ul_ee52c8aae5ce8db626e2d61db4d0bb79 |
| identifier.url.fl_str_mv | http://hdl.handle.net/10400.5/27675 |
| instacron_str | ul |
| institution | Universidade de Lisboa |
| instname_str | Universidade de Lisboa |
| language | eng |
| network_acronym_str | ul |
| network_name_str | Repositório da Universidade de Lisboa |
| oai_identifier_str | oai:repositorio.ulisboa.pt:10400.5/27675 |
| organization_str_mv | urn:organizationAcronym:ul |
| person_str_mv | Caiado, Jorge Crato, Nuno Peña, Daniel |
| publishDate | 2006 |
| publisher.none.fl_str_mv | Elsevier |
| reponame_str | Repositório da Universidade de Lisboa |
| repository_id_str | urn:repositoryAcronym:ul |
| service_str_mv | urn:repositoryAcronym:ul |
| spelling | engElsevierpt_PTThe statistical discrimination and clustering literature has studied the problem of identifying similarities in time series data. Some studies use non-parametric approaches for splitting a set of time series into clusters by looking at their Euclidean distances in the space of points. A new measure of distance between time series based on the normalized periodogram is proposed. Simulation results comparing this measure with others parametric and non-parametric metrics are provided. In particular, the classification of time series as stationary or as non-stationary is discussed. The use of both hierarchical and non-hierarchical clustering algorithms is considered. An illustrative example with economic time series data is also presented.application/pdfpt_PTA periodogram-based metric for time series classificationCaiado, JorgeCrato, NunoPeña, DanielHostingInstitutionOrganizationalRepositório Científico de Acesso Aberto da ULisboae-mailmailto:repositorio@reitoria.ulisboa.ptrepositorio@reitoria.ulisboa.ptISSNIsPartOf0167-9473DOIIsPartOf10.1016/j.csda.2005.04.0122023-04-28T16:04:17Z20062006-01-01T00:00:00ZHandlehttp://hdl.handle.net/10400.5/27675http://purl.org/coar/access_right/c_abf2open accessAutocorrelation FunctionClassificationClusteringEuclidean DistancePeriodogramStationary and Non-stationary Time Series230989 bytesliteraturehttp://purl.org/coar/resource_type/c_6501journal articlehttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://repositorio.ulisboa.pt/bitstreams/3719622b-803c-4fbb-8a93-416b527b380a/download |
| spellingShingle | A periodogram-based metric for time series classification Caiado, Jorge Autocorrelation Function Classification Clustering Euclidean Distance Periodogram Stationary and Non-stationary Time Series |
| status | SINGLETON |
| subject.fl_str_mv | Autocorrelation Function Classification Clustering Euclidean Distance Periodogram Stationary and Non-stationary Time Series |
| title | A periodogram-based metric for time series classification |
| title_full | A periodogram-based metric for time series classification |
| title_fullStr | A periodogram-based metric for time series classification |
| title_full_unstemmed | A periodogram-based metric for time series classification |
| title_short | A periodogram-based metric for time series classification |
| title_sort | A periodogram-based metric for time series classification |
| topic | Autocorrelation Function Classification Clustering Euclidean Distance Periodogram Stationary and Non-stationary Time Series |
| topic_facet | Autocorrelation Function Classification Clustering Euclidean Distance Periodogram Stationary and Non-stationary Time Series |
| url | http://hdl.handle.net/10400.5/27675 |
| visible | 1 |