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Forecasting tourism demand with artificial neural networks

dc.contributor.authorFernandes, Paula Odete
dc.contributor.authorTeixeira, João Paulo
dc.contributor.authorFerreira, João José
dc.contributor.authorAzevedo, Susana Garrido
dc.date.accessioned2012-08-28T10:21:01Z
dc.date.available2012-08-28T10:21:01Z
dc.date.issued2011
dc.description.abstractTourism has been viewed as an important player for the economic redevelopment of certain rural regions because of the attraction of landscapes, mountain, and the interest in second-home or investment opportunities at lower prices (Jackson & Murphy, 2002). Even with tourism‟s potential for development at all levels, there have been very few studies regarding models for estimating the local impact of tourism (Jackson & Murphy, 2006). To enhance understanding of the nature of forecasting in tourism destinations it is valuable to study systematically the possible estimative of influence that tourism destination has on an area. The main objective of this study is to present a set of models for tourism destinations competitiveness, using the Artificial Neural Networks methodology. This study focuses on two Portuguese regions - North and Centre - as tourism destinations offering a large range of tourist products, that goes beyond the beach, the mountains, the thermals not forgetting the rural tourism that has growing in the last years. These tourism destinations offer an interesting alternative to the „mass tourism‟ and have become more competitive, since the last one is normally associated with negative environmental impacts.por
dc.identifier.citationFernandes, Paula O.; Teixeira, João Paulo; Ferreira, João José; Azevedo, Susana Garrido (2011). Forecasting tourism demand with artificial neural networks. In 1st International Conference on Tourism & Management Studies. Algarve, Portugal. p. 41. ISBN: 978-989-84-72-13-7.por
dc.identifier.isbn978-989-84-72-13-7
dc.identifier.urihttp://hdl.handle.net/10198/7403
dc.language.isoengpor
dc.peerreviewedyespor
dc.subjectArtificial neural networkspor
dc.subjectNonlinear time seriespor
dc.subjectModellingpor
dc.subjectTourism forecastingpor
dc.titleForecasting tourism demand with artificial neural networkspor
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceAlgarvepor
oaire.citation.startPage41por
oaire.citation.title1st International Conference on Tourism & Management Studiespor
person.familyNameFernandes
person.familyNameTeixeira
person.givenNamePaula Odete
person.givenNameJoão
person.identifierN-3804-2013
person.identifier663194
person.identifier.ciencia-id991D-9D1E-D67D
person.identifier.ciencia-id4F15-B322-59B4
person.identifier.orcid0000-0001-8714-4901
person.identifier.orcid0000-0002-6679-5702
person.identifier.ridN-6576-2013
person.identifier.scopus-author-id35200741800
person.identifier.scopus-author-id57069567500
rcaap.rightsopenAccesspor
rcaap.typeconferenceObjectpor
relation.isAuthorOfPublication2269147c-2b53-4d1c-bc1b-f1367d197262
relation.isAuthorOfPublication33f4af65-7ddf-46f0-8b44-a7470a8ba2bf
relation.isAuthorOfPublication.latestForDiscovery2269147c-2b53-4d1c-bc1b-f1367d197262

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