Publicação
Modelação da procura turística: um estudo comparativo entre redes neuronais artificiais e a metodologia de Box-Jenkins
| Resumo: | The present research aims to explore and to evidence the utility of the methodology of Artificial Neural Networks (ANN) in the analysis of tourism demand as an alternative to the Box-Jenkins methodology. The first methodology has arising interest in the economic and business area since several researches have verified that methodology presents a valid alternative to classical methods of forecasting allowing giving answer to situations in which the traditional ones will be of difficult to apply (Thawornwong & Enke, 2004). According to Hill et al. (1996) and Hansen et al. (1999) ANN show capacity to improve the time-series forecasts through of additional information analysis decreasing their dimension and reducing their complexity. For that, each one of the referred methodologies focused in the treatment, analysis and modeling of the tourism time-series: Monthly Guest Nights in Hotels registered between January 1987 to December 2006, since it is one of the variables that better explain the effective tourism demand. The Study was performed for two regions of Portugal: North region and Centre region. Considering the results, and according to the Criteria of MAPE for model evaluation proposed by Lewis (1982), the ANN model presented acceptable statistical qualities and adjustments satisfied. Being so, it is adequate not only for the modelling but also to the prediction of times series, when compared to the model performed by Box- Jenkins methodology. We intended also to evaluate the performance and competiveness of the tourism destinations - North region and Center region of Portugal - by main origin markets and to analyse how it is distributed their portfolio of origin markets for the period of 1997 to 2006. The Market Share Analysis tool proposed by Faulkner (1997) was applied and it was observed an high dependency of the domestic market for both regions. |
|---|---|
| Autores principais: | Fernandes, Paula Odete |
| Outros Autores: | Teixeira, João Paulo; Ferreira, João José; Azevedo, Susana Garrido |
| Assunto: | Artificial neural networks ARIMA models Time series forecasts Tourism destinations Competitiveness Market share Redes neuronais artificiais Modelos ARIMA Previsão de séries temporais Destinos turísticos Competitividade Quotas de mercado |
| Ano: | 2008 |
| País: | Portugal |
| Tipo de documento: | comunicação em conferência |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Instituto Politécnico de Bragança |
| Idioma: | português |
| Origem: | Biblioteca Digital do IPB |
| _version_ | 1867173098728456192 |
|---|---|
| author | Fernandes, Paula Odete |
| author2 | Teixeira, João Paulo Ferreira, João José Azevedo, Susana Garrido |
| author2_role | author author author |
| author_facet | Fernandes, Paula Odete Teixeira, João Paulo Ferreira, João José Azevedo, Susana Garrido |
| author_role | author |
| contributor_name_str_mv | Biblioteca Digital do IPB |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Fernandes, Paula Odete\",\"Person.identifier.orcid\":\"0000-0001-8714-4901\"},{\"Person.name\":\"Teixeira, João Paulo\",\"Person.identifier.orcid\":\"0000-0002-6679-5702\"},{\"Person.name\":\"Ferreira, João José\"},{\"Person.name\":\"Azevedo, Susana Garrido\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Biblioteca Digital do IPB |
| datacite.creators.creator.creatorName.fl_str_mv | Fernandes, Paula Odete Teixeira, João Paulo Ferreira, João José Azevedo, Susana Garrido |
| datacite.date.Accepted.fl_str_mv | 2008-01-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2009-02-05T16:18:24Z |
| datacite.date.embargoed.fl_str_mv | 2009-02-05T16:18:24Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Artificial neural networks ARIMA models Time series forecasts Tourism destinations Competitiveness Market share Redes neuronais artificiais Modelos ARIMA Previsão de séries temporais Destinos turísticos Competitividade Quotas de mercado |
| datacite.titles.title.fl_str_mv | Modelação da procura turística: um estudo comparativo entre redes neuronais artificiais e a metodologia de Box-Jenkins |
| dc.contributor.none.fl_str_mv | Biblioteca Digital do IPB |
| dc.creator.none.fl_str_mv | Fernandes, Paula Odete Teixeira, João Paulo Ferreira, João José Azevedo, Susana Garrido |
| dc.date.Accepted.fl_str_mv | 2008-01-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2009-02-05T16:18:24Z |
| dc.date.embargoed.fl_str_mv | 2009-02-05T16:18:24Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10198/1035 |
| dc.language.none.fl_str_mv | por |
| dc.publisher.none.fl_str_mv | Universidad de Baja California |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | Artificial neural networks ARIMA models Time series forecasts Tourism destinations Competitiveness Market share Redes neuronais artificiais Modelos ARIMA Previsão de séries temporais Destinos turísticos Competitividade Quotas de mercado |
| dc.title.fl_str_mv | Modelação da procura turística: um estudo comparativo entre redes neuronais artificiais e a metodologia de Box-Jenkins |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_5794 |
| description | The present research aims to explore and to evidence the utility of the methodology of Artificial Neural Networks (ANN) in the analysis of tourism demand as an alternative to the Box-Jenkins methodology. The first methodology has arising interest in the economic and business area since several researches have verified that methodology presents a valid alternative to classical methods of forecasting allowing giving answer to situations in which the traditional ones will be of difficult to apply (Thawornwong & Enke, 2004). According to Hill et al. (1996) and Hansen et al. (1999) ANN show capacity to improve the time-series forecasts through of additional information analysis decreasing their dimension and reducing their complexity. For that, each one of the referred methodologies focused in the treatment, analysis and modeling of the tourism time-series: Monthly Guest Nights in Hotels registered between January 1987 to December 2006, since it is one of the variables that better explain the effective tourism demand. The Study was performed for two regions of Portugal: North region and Centre region. Considering the results, and according to the Criteria of MAPE for model evaluation proposed by Lewis (1982), the ANN model presented acceptable statistical qualities and adjustments satisfied. Being so, it is adequate not only for the modelling but also to the prediction of times series, when compared to the model performed by Box- Jenkins methodology. We intended also to evaluate the performance and competiveness of the tourism destinations - North region and Center region of Portugal - by main origin markets and to analyse how it is distributed their portfolio of origin markets for the period of 1997 to 2006. The Market Share Analysis tool proposed by Faulkner (1997) was applied and it was observed an high dependency of the domestic market for both regions. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | conferencePaper |
| fulltext.url.fl_str_mv | https://bibliotecadigital.ipb.pt/bitstreams/e6810d83-7ce2-4774-b9da-6684ea92a5b6/download |
| id | ipb_a35e360aa9ba4cdce13932184d5c8a88 |
| identifier.url.fl_str_mv | http://hdl.handle.net/10198/1035 |
| instacron_str | ipb |
| institution | Instituto Politécnico de Bragança |
| instname_str | Instituto Politécnico de Bragança |
| language | por |
| network_acronym_str | ipb |
| network_name_str | Biblioteca Digital do IPB |
| oai_identifier_str | oai:bibliotecadigital.ipb.pt:10198/1035 |
| organization_str_mv | urn:organizationAcronym:ipb |
| person_str_mv | Fernandes, Paula Odete Fernandes, Paula Odete https://www.ciencia-id.pt/991D-9D1E-D67D 991D-9D1E-D67D http://orcid.org/0000-0001-8714-4901 0000-0001-8714-4901 Teixeira, João Paulo Teixeira, João Paulo https://www.ciencia-id.pt/4F15-B322-59B4 4F15-B322-59B4 http://orcid.org/0000-0002-6679-5702 0000-0002-6679-5702 Ferreira, João José Azevedo, Susana Garrido |
| publishDate | 2008 |
| publisher.none.fl_str_mv | Universidad de Baja California |
| reponame_str | Biblioteca Digital do IPB |
| repository_id_str | urn:repositoryAcronym:ipb |
| service_str_mv | urn:repositoryAcronym:ipb |
| spelling | porUniversidad de Baja CaliforniaenThe present research aims to explore and to evidence the utility of the methodology of Artificial Neural Networks (ANN) in the analysis of tourism demand as an alternative to the Box-Jenkins methodology. The first methodology has arising interest in the economic and business area since several researches have verified that methodology presents a valid alternative to classical methods of forecasting allowing giving answer to situations in which the traditional ones will be of difficult to apply (Thawornwong & Enke, 2004). According to Hill et al. (1996) and Hansen et al. (1999) ANN show capacity to improve the time-series forecasts through of additional information analysis decreasing their dimension and reducing their complexity. For that, each one of the referred methodologies focused in the treatment, analysis and modeling of the tourism time-series: Monthly Guest Nights in Hotels registered between January 1987 to December 2006, since it is one of the variables that better explain the effective tourism demand. The Study was performed for two regions of Portugal: North region and Centre region. Considering the results, and according to the Criteria of MAPE for model evaluation proposed by Lewis (1982), the ANN model presented acceptable statistical qualities and adjustments satisfied. Being so, it is adequate not only for the modelling but also to the prediction of times series, when compared to the model performed by Box- Jenkins methodology. We intended also to evaluate the performance and competiveness of the tourism destinations - North region and Center region of Portugal - by main origin markets and to analyse how it is distributed their portfolio of origin markets for the period of 1997 to 2006. The Market Share Analysis tool proposed by Faulkner (1997) was applied and it was observed an high dependency of the domestic market for both regions.application/pdfenModelação da procura turística: um estudo comparativo entre redes neuronais artificiais e a metodologia de Box-JenkinsPersonalFernandes, Paula OdeteDSpacehttp://dspace.org/items/2269147c-2b53-4d1c-bc1b-f1367d197262DSpacehttp://dspace.org/items/2269147c-2b53-4d1c-bc1b-f1367d197262FernandesPaula OdeteCiência IDhttps://www.ciencia-id.pt991D-9D1E-D67DORCIDhttp://orcid.org0000-0001-8714-4901Scopus Author IDhttps://www.scopus.com35200741800PersonalTeixeira, João PauloDSpacehttp://dspace.org/items/33f4af65-7ddf-46f0-8b44-a7470a8ba2bfDSpacehttp://dspace.org/items/33f4af65-7ddf-46f0-8b44-a7470a8ba2bfTeixeiraJoão PauloCiência IDhttps://www.ciencia-id.pt4F15-B322-59B4ORCIDhttp://orcid.org0000-0002-6679-5702Researcher IDhttps://www.researcherid.comN-6576-2013Scopus Author IDhttps://www.scopus.com57069567500Ferreira, João JoséAzevedo, Susana GarridoHostingInstitutionOrganizationalBiblioteca Digital do IPBe-mailmailto:dspace@ipb.ptdspace@ipb.pt2009-02-05T16:18:24Z20082008-01-01T00:00:00ZHandlehttp://hdl.handle.net/10198/1035http://purl.org/coar/access_right/c_abf2open accessArtificial neural networksARIMA modelsTime series forecastsTourism destinationsCompetitivenessMarket shareRedes neuronais artificiaisModelos ARIMAPrevisão de séries temporaisDestinos turísticosCompetitividadeQuotas de mercado334867 bytesother research producthttp://purl.org/coar/resource_type/c_5794conference paperhttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://bibliotecadigital.ipb.pt/bitstreams/e6810d83-7ce2-4774-b9da-6684ea92a5b6/download |
| spellingShingle | Modelação da procura turística: um estudo comparativo entre redes neuronais artificiais e a metodologia de Box-Jenkins Fernandes, Paula Odete Artificial neural networks ARIMA models Time series forecasts Tourism destinations Competitiveness Market share Redes neuronais artificiais Modelos ARIMA Previsão de séries temporais Destinos turísticos Competitividade Quotas de mercado |
| status | SINGLETON |
| subject.fl_str_mv | Artificial neural networks ARIMA models Time series forecasts Tourism destinations Competitiveness Market share Redes neuronais artificiais Modelos ARIMA Previsão de séries temporais Destinos turísticos Competitividade Quotas de mercado |
| title | Modelação da procura turística: um estudo comparativo entre redes neuronais artificiais e a metodologia de Box-Jenkins |
| title_full | Modelação da procura turística: um estudo comparativo entre redes neuronais artificiais e a metodologia de Box-Jenkins |
| title_fullStr | Modelação da procura turística: um estudo comparativo entre redes neuronais artificiais e a metodologia de Box-Jenkins |
| title_full_unstemmed | Modelação da procura turística: um estudo comparativo entre redes neuronais artificiais e a metodologia de Box-Jenkins |
| title_short | Modelação da procura turística: um estudo comparativo entre redes neuronais artificiais e a metodologia de Box-Jenkins |
| title_sort | Modelação da procura turística: um estudo comparativo entre redes neuronais artificiais e a metodologia de Box-Jenkins |
| topic | Artificial neural networks ARIMA models Time series forecasts Tourism destinations Competitiveness Market share Redes neuronais artificiais Modelos ARIMA Previsão de séries temporais Destinos turísticos Competitividade Quotas de mercado |
| topic_facet | Artificial neural networks ARIMA models Time series forecasts Tourism destinations Competitiveness Market share Redes neuronais artificiais Modelos ARIMA Previsão de séries temporais Destinos turísticos Competitividade Quotas de mercado |
| url | http://hdl.handle.net/10198/1035 |
| visible | 1 |