New developments in the forecasting of monthly overnight stays in the North Region of Portugal

Detalhes bibliográficos
Autor(a) principal: Silva, Isabel
Data de Publicação: 2020
Outros Autores: Alonso, Hugo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10773/29229
Resumo: The Tourism sector is of strategic importance to the North Region of Portugal and is growing. Forecasting monthly overnight stays in this region is, therefore, a relevant problem. In this paper, we analyze data more recent than those considered in previous studies and use them to develop and compare several forecasting models and methods. We conclude that the best results are achieved by models based on a non-parametric approach not considered so far for these data, the singular spectrum analysis.
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spelling New developments in the forecasting of monthly overnight stays in the North Region of PortugalForecastingNeural networksOvernight staysSingular spectrum analysisTime seriesThe Tourism sector is of strategic importance to the North Region of Portugal and is growing. Forecasting monthly overnight stays in this region is, therefore, a relevant problem. In this paper, we analyze data more recent than those considered in previous studies and use them to develop and compare several forecasting models and methods. We conclude that the best results are achieved by models based on a non-parametric approach not considered so far for these data, the singular spectrum analysis.Taylor & Francis2021-07-21T00:00:00Z2020-01-01T00:00:00Z2020info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10773/29229eng0266-476310.1080/02664763.2020.1795812Silva, IsabelAlonso, Hugoinfo: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:RCAAP2024-02-22T11:56:34Zoai:ria.ua.pt:10773/29229Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:01:37.710238Repositório 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 New developments in the forecasting of monthly overnight stays in the North Region of Portugal
title New developments in the forecasting of monthly overnight stays in the North Region of Portugal
spellingShingle New developments in the forecasting of monthly overnight stays in the North Region of Portugal
Silva, Isabel
Forecasting
Neural networks
Overnight stays
Singular spectrum analysis
Time series
title_short New developments in the forecasting of monthly overnight stays in the North Region of Portugal
title_full New developments in the forecasting of monthly overnight stays in the North Region of Portugal
title_fullStr New developments in the forecasting of monthly overnight stays in the North Region of Portugal
title_full_unstemmed New developments in the forecasting of monthly overnight stays in the North Region of Portugal
title_sort New developments in the forecasting of monthly overnight stays in the North Region of Portugal
author Silva, Isabel
author_facet Silva, Isabel
Alonso, Hugo
author_role author
author2 Alonso, Hugo
author2_role author
dc.contributor.author.fl_str_mv Silva, Isabel
Alonso, Hugo
dc.subject.por.fl_str_mv Forecasting
Neural networks
Overnight stays
Singular spectrum analysis
Time series
topic Forecasting
Neural networks
Overnight stays
Singular spectrum analysis
Time series
description The Tourism sector is of strategic importance to the North Region of Portugal and is growing. Forecasting monthly overnight stays in this region is, therefore, a relevant problem. In this paper, we analyze data more recent than those considered in previous studies and use them to develop and compare several forecasting models and methods. We conclude that the best results are achieved by models based on a non-parametric approach not considered so far for these data, the singular spectrum analysis.
publishDate 2020
dc.date.none.fl_str_mv 2020-01-01T00:00:00Z
2020
2021-07-21T00:00:00Z
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/10773/29229
url http://hdl.handle.net/10773/29229
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0266-4763
10.1080/02664763.2020.1795812
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 Taylor & Francis
publisher.none.fl_str_mv Taylor & Francis
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
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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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