Comparison of selection and combination strategies for demand forecasting methods

Detalhes bibliográficos
Autor(a) principal: Bandeira,Saymon Galvão
Data de Publicação: 2020
Outros Autores: Alcalá,Symone Gomes Soares, Vita,Roberto Oliveira, Barbosa,Talles Marcelo Gonçalves de Andrade
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Production
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-65132020000100210
Resumo: Abstract Paper aims In this study, effective strategies to combine and select forecasting methods are proposed. In the selection strategy, the best performing forecasting method from a pool of methods is selected based on its accuracy, whereas the combination strategies are based on the mean methods’ outputs and on the methods’ accuracy. Originality Despite the large amount of work in this area, the actual literature lacks of selection and combination strategies of forecasting methods for dealing with intermittent time series. Research method The included forecasting methods are state-of-the-art approaches applied to industrial and academics forecasting problems. Experiments were performed to evaluate the performance of the proposed strategies using a spare part data set of an industry of elevators and a data set from the M3-Competition. Main findings The results show that, in most cases, the accuracy of the demand forecasts can be improved when using the proposed selection and combination strategies. Implications for theory and practice The proposed methodology can be applied to forecasting problems, covering a variety of characteristics (e.g., intermittency, trend). The results reveal that combination strategies have potential application, perform better than state-of-the-art models, and have comparable accuracy in intermittent series. Thus, they can be employed to improve production planning activities.
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spelling Comparison of selection and combination strategies for demand forecasting methodsTime series forecastingForecast uncertaintyTechnology forecastingCombination strategiesForecasting method selectionAbstract Paper aims In this study, effective strategies to combine and select forecasting methods are proposed. In the selection strategy, the best performing forecasting method from a pool of methods is selected based on its accuracy, whereas the combination strategies are based on the mean methods’ outputs and on the methods’ accuracy. Originality Despite the large amount of work in this area, the actual literature lacks of selection and combination strategies of forecasting methods for dealing with intermittent time series. Research method The included forecasting methods are state-of-the-art approaches applied to industrial and academics forecasting problems. Experiments were performed to evaluate the performance of the proposed strategies using a spare part data set of an industry of elevators and a data set from the M3-Competition. Main findings The results show that, in most cases, the accuracy of the demand forecasts can be improved when using the proposed selection and combination strategies. Implications for theory and practice The proposed methodology can be applied to forecasting problems, covering a variety of characteristics (e.g., intermittency, trend). The results reveal that combination strategies have potential application, perform better than state-of-the-art models, and have comparable accuracy in intermittent series. Thus, they can be employed to improve production planning activities.Associação Brasileira de Engenharia de Produção2020-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-65132020000100210Production v.30 2020reponame:Productioninstname:Associação Brasileira de Engenharia de Produção (ABEPRO)instacron:ABEPRO10.1590/0103-6513.20200009info:eu-repo/semantics/openAccessBandeira,Saymon GalvãoAlcalá,Symone Gomes SoaresVita,Roberto OliveiraBarbosa,Talles Marcelo Gonçalves de Andradeeng2020-09-17T00:00:00Zoai:scielo:S0103-65132020000100210Revistahttps://www.scielo.br/j/prod/https://old.scielo.br/oai/scielo-oai.php||production@editoracubo.com.br1980-54110103-6513opendoar:2020-09-17T00:00Production - Associação Brasileira de Engenharia de Produção (ABEPRO)false
dc.title.none.fl_str_mv Comparison of selection and combination strategies for demand forecasting methods
title Comparison of selection and combination strategies for demand forecasting methods
spellingShingle Comparison of selection and combination strategies for demand forecasting methods
Bandeira,Saymon Galvão
Time series forecasting
Forecast uncertainty
Technology forecasting
Combination strategies
Forecasting method selection
title_short Comparison of selection and combination strategies for demand forecasting methods
title_full Comparison of selection and combination strategies for demand forecasting methods
title_fullStr Comparison of selection and combination strategies for demand forecasting methods
title_full_unstemmed Comparison of selection and combination strategies for demand forecasting methods
title_sort Comparison of selection and combination strategies for demand forecasting methods
author Bandeira,Saymon Galvão
author_facet Bandeira,Saymon Galvão
Alcalá,Symone Gomes Soares
Vita,Roberto Oliveira
Barbosa,Talles Marcelo Gonçalves de Andrade
author_role author
author2 Alcalá,Symone Gomes Soares
Vita,Roberto Oliveira
Barbosa,Talles Marcelo Gonçalves de Andrade
author2_role author
author
author
dc.contributor.author.fl_str_mv Bandeira,Saymon Galvão
Alcalá,Symone Gomes Soares
Vita,Roberto Oliveira
Barbosa,Talles Marcelo Gonçalves de Andrade
dc.subject.por.fl_str_mv Time series forecasting
Forecast uncertainty
Technology forecasting
Combination strategies
Forecasting method selection
topic Time series forecasting
Forecast uncertainty
Technology forecasting
Combination strategies
Forecasting method selection
description Abstract Paper aims In this study, effective strategies to combine and select forecasting methods are proposed. In the selection strategy, the best performing forecasting method from a pool of methods is selected based on its accuracy, whereas the combination strategies are based on the mean methods’ outputs and on the methods’ accuracy. Originality Despite the large amount of work in this area, the actual literature lacks of selection and combination strategies of forecasting methods for dealing with intermittent time series. Research method The included forecasting methods are state-of-the-art approaches applied to industrial and academics forecasting problems. Experiments were performed to evaluate the performance of the proposed strategies using a spare part data set of an industry of elevators and a data set from the M3-Competition. Main findings The results show that, in most cases, the accuracy of the demand forecasts can be improved when using the proposed selection and combination strategies. Implications for theory and practice The proposed methodology can be applied to forecasting problems, covering a variety of characteristics (e.g., intermittency, trend). The results reveal that combination strategies have potential application, perform better than state-of-the-art models, and have comparable accuracy in intermittent series. Thus, they can be employed to improve production planning activities.
publishDate 2020
dc.date.none.fl_str_mv 2020-01-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-65132020000100210
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-65132020000100210
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/0103-6513.20200009
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Associação Brasileira de Engenharia de Produção
publisher.none.fl_str_mv Associação Brasileira de Engenharia de Produção
dc.source.none.fl_str_mv Production v.30 2020
reponame:Production
instname:Associação Brasileira de Engenharia de Produção (ABEPRO)
instacron:ABEPRO
instname_str Associação Brasileira de Engenharia de Produção (ABEPRO)
instacron_str ABEPRO
institution ABEPRO
reponame_str Production
collection Production
repository.name.fl_str_mv Production - Associação Brasileira de Engenharia de Produção (ABEPRO)
repository.mail.fl_str_mv ||production@editoracubo.com.br
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