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Tourism demand forecasting with neural network models: different ways of treating information
dc.contributor.author | Claveria, Oscar |
dc.contributor.author | Monte Moreno, Enrique |
dc.contributor.author | Torra Porras, Salvador |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions |
dc.date.accessioned | 2014-07-23T06:55:25Z |
dc.date.created | 2014-07-21 |
dc.date.issued | 2014-07-21 |
dc.identifier.citation | Claveria, O.; Monte, E.; Torra Porras, S. Tourism demand forecasting with neural network models: different ways of treating information. "International journal of tourism research", 21 Juliol 2014, vol. 17, núm. 3, p. 209-312. |
dc.identifier.issn | 1099-2340 |
dc.identifier.uri | http://hdl.handle.net/2117/23584 |
dc.description.abstract | This paper aims to compare the performance of three different artificial neural network techniques for tourist demand forecasting: a multi-layer perceptron, a radial basis function and an Elman network. We find that multi-layer perceptron and radial basis function models outperform Elman networks. We repeated the experiment assuming different topologies regarding the number of lags used for concatenation so as to evaluate the effect of the memory on the forecasting results. We find that for higher memories, the forecasting performance obtained for longer horizons improves, suggesting the importance of increasing the dimensionality for long-term forecasting. |
dc.language.iso | eng |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Spain |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
dc.subject | Àrees temàtiques de la UPC::Enginyeria de la telecomunicació |
dc.subject.lcsh | Neural networks (Computer science) |
dc.subject.lcsh | Artificial intelligence |
dc.subject.other | Tourism demand |
dc.subject.other | Forecasting |
dc.subject.other | Artificial neural networks |
dc.subject.other | Multi-layer perceptron |
dc.subject.other | Radial basis function |
dc.subject.other | Elman networks |
dc.title | Tourism demand forecasting with neural network models: different ways of treating information |
dc.type | Article |
dc.subject.lemac | Xarxes neuronals (Informàtica) |
dc.subject.lemac | Intel·ligència artificial |
dc.contributor.group | Universitat Politècnica de Catalunya. VEU - Grup de Tractament de la Parla |
dc.identifier.doi | 10.1002/jtr.2016 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | http://onlinelibrary.wiley.com/doi/10.1002/jtr.2016/abstract |
dc.rights.access | Restricted access - publisher's policy |
local.identifier.drac | 15013651 |
dc.description.version | Postprint (published version) |
dc.date.lift | 10000-01-01 |
local.citation.author | Claveria, O.; Monte, E.; Torra Porras, S. |
local.citation.publicationName | International journal of tourism research |
local.citation.volume | 17 |
local.citation.number | 3 |
local.citation.startingPage | 209 |
local.citation.endingPage | 312 |
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