Forecasting seasonal time series with computational intelligence: on recent methods and the potential of their combinations

Detalhes bibliográficos
Autor(a) principal: Stepnicka, M.
Data de Publicação: 2013
Outros Autores: Cortez, Paulo, Peralta Donate, Juan, Stepnickova, Lenka
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/1822/23527
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.
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spelling Forecasting seasonal time series with computational intelligence: on recent methods and the potential of their combinationsTime seriesComputational intelligenceNeural networksSupport vector machineFuzzy rulesGenetic algorithmScience & TechnologyAccurate 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.The research was supported by the European Regional Development Fund in the IT4Innovations Centre of Excellence project (CZ.1.05/1.1.00/02.0070). Furthermore, we gratefully acknowledge partial support of the project KON- TAKT II - LH12229 of MSˇMT CˇR.ElsevierUniversidade do MinhoStepnicka, M.Cortez, PauloPeralta Donate, JuanStepnickova, Lenka2013-052013-05-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/23527eng0957-417410.1016/j.eswa.2012.10.001http://dx.doi.org/10.1016/j.eswa.2012.10.001info: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:RCAAP2023-07-21T11:55:08Zoai:repositorium.sdum.uminho.pt:1822/23527Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T18:44:39.116278Repositório 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 Forecasting seasonal time series with computational intelligence: on recent methods and the potential of their combinations
title Forecasting seasonal time series with computational intelligence: on recent methods and the potential of their combinations
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
Science & Technology
title_short 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_sort Forecasting seasonal time series with computational intelligence: on recent methods and the potential of their combinations
author Stepnicka, M.
author_facet Stepnicka, M.
Cortez, Paulo
Peralta Donate, Juan
Stepnickova, Lenka
author_role author
author2 Cortez, Paulo
Peralta Donate, Juan
Stepnickova, Lenka
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Stepnicka, M.
Cortez, Paulo
Peralta Donate, Juan
Stepnickova, Lenka
dc.subject.por.fl_str_mv Time series
Computational intelligence
Neural networks
Support vector machine
Fuzzy rules
Genetic algorithm
Science & Technology
topic Time series
Computational intelligence
Neural networks
Support vector machine
Fuzzy rules
Genetic algorithm
Science & Technology
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.
publishDate 2013
dc.date.none.fl_str_mv 2013-05
2013-05-01T00:00:00Z
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/1822/23527
url http://hdl.handle.net/1822/23527
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0957-4174
10.1016/j.eswa.2012.10.001
http://dx.doi.org/10.1016/j.eswa.2012.10.001
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 Elsevier
publisher.none.fl_str_mv Elsevier
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
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