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Training neural networks by resilient backpropagation algorithm for tourism forecasting

dc.contributor.authorFernandes, Paula Odete
dc.contributor.authorTeixeira, João Paulo
dc.contributor.authorFerreira, João José
dc.contributor.authorAzevedo, Susana Garrido
dc.date.accessioned2014-06-23T10:46:05Z
dc.date.available2014-06-23T10:46:05Z
dc.date.issued2013
dc.description.abstractThe main objective of this study is to presents a set of models for tourism destinations competitiveness, using the Artificial Neural Networks (ANN) methodology. The time series of two regions (North and Centre of Portugal) has used to predict the tourism demand. The prediction for two years ahead gives a mean absolute percentage error between 5 and 9 %. Therefore, the ANN model is adequate for modelling and prediction of the reference time series. This model is an important and useful framework for better planning and development of these two regions as they operate in highly competitive markets.por
dc.identifier.citationFernandes, Paula O.; Teixeira, João Paulo;, Ferreira, João; Azevedo, Susana (2013). Training neural networks by resilient backpropagation algorithm for tourism forecasting. Management Intelligent Systems, Advances in Intelligent Systems and Computing, Springer. 220, p. 41-49. ISBN 978-3-319-00569-0por
dc.identifier.doi10.1007/978-3-319-00569-0_6
dc.identifier.urihttp://hdl.handle.net/10198/9736
dc.language.isoengpor
dc.peerreviewedyespor
dc.publisherSpringerpor
dc.subjectNeural networkspor
dc.subjectTime series analysispor
dc.subjectTourism demand forecastingpor
dc.subjectResilient backpropagation algorithmpor
dc.titleTraining neural networks by resilient backpropagation algorithm for tourism forecastingpor
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage49por
oaire.citation.startPage41por
oaire.citation.titleManagement Intelligent Systems, Advances in Intelligent Systems and Computingpor
oaire.citation.volume220por
person.familyNameFernandes
person.familyNameTeixeira
person.givenNamePaula Odete
person.givenNameJoão Paulo
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.rightsrestrictedAccesspor
rcaap.typearticlepor
relation.isAuthorOfPublication2269147c-2b53-4d1c-bc1b-f1367d197262
relation.isAuthorOfPublication33f4af65-7ddf-46f0-8b44-a7470a8ba2bf
relation.isAuthorOfPublication.latestForDiscovery33f4af65-7ddf-46f0-8b44-a7470a8ba2bf

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