Publicação
Forecasting seasonal time series with computational intelligence: on recent methods and the potential of their combinations
| Resumo: | Accurate time series forecasting is a key issue to support individual and or- ganizational decision making. In this paper, we introduce novel methods for multi-step seasonal time series forecasting. All the presented methods stem from computational intelligence techniques: evolutionary artificial neu- ral networks, support vector machines and genuine linguistic fuzzy rules. Performance of the suggested methods is experimentally justified on sea- sonal time series from distinct domains on three forecasting horizons. The most important contribution is the introduction of a new hybrid combination using linguistic fuzzy rules and the other computational intelligence methods. This hybrid combination presents competitive forecasts, when compared with the popular ARIMA method. Moreover, such hybrid model is more easy to interpret by decision-makers when modeling trended series. |
|---|---|
| Autores principais: | Stepnicka, M. |
| Outros Autores: | Cortez, Paulo; Peralta Donate, Juan; Stepnickova, Lenka |
| Assunto: | Time series Computational intelligence Neural networks Support vector machine Fuzzy rules Genetic algorithm |
| Ano: | 2013 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| _version_ | 1867437893574721536 |
|---|---|
| author | Stepnicka, M. |
| author2 | Cortez, Paulo Peralta Donate, Juan Stepnickova, Lenka |
| author2_role | author author author |
| author_facet | Stepnicka, M. Cortez, Paulo Peralta Donate, Juan Stepnickova, Lenka |
| author_role | author |
| contributor_name_str_mv | RepositóriUM - Universidade do Minho |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Stepnicka, M.\"},{\"Person.name\":\"Cortez, Paulo\"},{\"Person.name\":\"Peralta Donate, Juan\"},{\"Person.name\":\"Stepnickova, Lenka\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | RepositóriUM - Universidade do Minho |
| datacite.creators.creator.creatorName.fl_str_mv | Stepnicka, M. Cortez, Paulo Peralta Donate, Juan Stepnickova, Lenka |
| datacite.date.Accepted.fl_str_mv | 2013-05-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2013-03-26T14:23:05Z |
| datacite.date.embargoed.fl_str_mv | 2013-03-26T14:23:05Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Time series Computational intelligence Neural networks Support vector machine Fuzzy rules Genetic algorithm |
| datacite.titles.title.fl_str_mv | Forecasting seasonal time series with computational intelligence: on recent methods and the potential of their combinations |
| dc.contributor.none.fl_str_mv | RepositóriUM - Universidade do Minho |
| dc.creator.none.fl_str_mv | Stepnicka, M. Cortez, Paulo Peralta Donate, Juan Stepnickova, Lenka |
| dc.date.Accepted.fl_str_mv | 2013-05-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2013-03-26T14:23:05Z |
| dc.date.embargoed.fl_str_mv | 2013-03-26T14:23:05Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | https://hdl.handle.net/1822/23527 |
| 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 | Time series Computational intelligence Neural networks Support vector machine Fuzzy rules Genetic algorithm |
| dc.title.fl_str_mv | Forecasting seasonal time series with computational intelligence: on recent methods and the potential of their combinations |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_6501 |
| description | Accurate time series forecasting is a key issue to support individual and or- ganizational decision making. In this paper, we introduce novel methods for multi-step seasonal time series forecasting. All the presented methods stem from computational intelligence techniques: evolutionary artificial neu- ral networks, support vector machines and genuine linguistic fuzzy rules. Performance of the suggested methods is experimentally justified on sea- sonal time series from distinct domains on three forecasting horizons. The most important contribution is the introduction of a new hybrid combination using linguistic fuzzy rules and the other computational intelligence methods. This hybrid combination presents competitive forecasts, when compared with the popular ARIMA method. Moreover, such hybrid model is more easy to interpret by decision-makers when modeling trended series. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | article |
| fulltext.url.fl_str_mv | https://repositorium.uminho.pt/bitstreams/b9ed0236-5290-437a-88e9-f70af2ed8164/download |
| id | rum_d0d7db3898bfb42ef2ebefbba061cda9 |
| identifier.url.fl_str_mv | https://hdl.handle.net/1822/23527 |
| instacron_str | repositorium |
| institution | Universidade do Minho |
| instname_str | Universidade do Minho |
| language | eng |
| network_acronym_str | rum |
| network_name_str | RepositóriUM - Universidade do Minho |
| oai_identifier_str | oai:repositorium.uminho.pt:1822/23527 |
| organization_str_mv | urn:organizationAcronym:repositorium |
| person_str_mv | Stepnicka, M. Cortez, Paulo Peralta Donate, Juan Stepnickova, Lenka |
| publishDate | 2013 |
| publisher.none.fl_str_mv | Elsevier |
| reponame_str | RepositóriUM - Universidade do Minho |
| repository_id_str | urn:repositoryAcronym:rum |
| service_str_mv | urn:repositoryAcronym:rum |
| spelling | engElsevierporAccurate time series forecasting is a key issue to support individual and or- ganizational decision making. In this paper, we introduce novel methods for multi-step seasonal time series forecasting. All the presented methods stem from computational intelligence techniques: evolutionary artificial neu- ral networks, support vector machines and genuine linguistic fuzzy rules. Performance of the suggested methods is experimentally justified on sea- sonal time series from distinct domains on three forecasting horizons. The most important contribution is the introduction of a new hybrid combination using linguistic fuzzy rules and the other computational intelligence methods. This hybrid combination presents competitive forecasts, when compared with the popular ARIMA method. Moreover, such hybrid model is more easy to interpret by decision-makers when modeling trended series.application/pdfporForecasting seasonal time series with computational intelligence: on recent methods and the potential of their combinationsStepnicka, M.Cortez, PauloPeralta Donate, JuanStepnickova, LenkaHostingInstitutionOrganizationalRepositóriUM - Universidade do Minhoe-mailmailto:repositorium@usdb.uminho.ptrepositorium@usdb.uminho.ptISSNIsPartOf0957-4174DOIIsPartOf10.1016/j.eswa.2012.10.0012013-03-26T14:23:05Z2013-052013-05-01T00:00:00ZHandlehttps://hdl.handle.net/1822/23527http://purl.org/coar/access_right/c_abf2open accessTime seriesComputational intelligenceNeural networksSupport vector machineFuzzy rulesGenetic algorithm454493 bytesliteraturehttp://purl.org/coar/resource_type/c_6501journal articlehttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://repositorium.uminho.pt/bitstreams/b9ed0236-5290-437a-88e9-f70af2ed8164/download |
| spellingShingle | Forecasting seasonal time series with computational intelligence: on recent methods and the potential of their combinations Stepnicka, M. Time series Computational intelligence Neural networks Support vector machine Fuzzy rules Genetic algorithm |
| status | SINGLETON |
| subject.fl_str_mv | Time series Computational intelligence Neural networks Support vector machine Fuzzy rules Genetic algorithm |
| title | Forecasting seasonal time series with computational intelligence: on recent methods and the potential of their combinations |
| title_full | Forecasting seasonal time series with computational intelligence: on recent methods and the potential of their combinations |
| title_fullStr | Forecasting seasonal time series with computational intelligence: on recent methods and the potential of their combinations |
| title_full_unstemmed | Forecasting seasonal time series with computational intelligence: on recent methods and the potential of their combinations |
| title_short | Forecasting seasonal time series with computational intelligence: on recent methods and the potential of their combinations |
| title_sort | Forecasting seasonal time series with computational intelligence: on recent methods and the potential of their combinations |
| topic | Time series Computational intelligence Neural networks Support vector machine Fuzzy rules Genetic algorithm |
| topic_facet | Time series Computational intelligence Neural networks Support vector machine Fuzzy rules Genetic algorithm |
| url | https://hdl.handle.net/1822/23527 |
| visible | 1 |