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dc.contributor.authorClaveria González, Oscar
dc.contributor.authorMonte Moreno, Enrique
dc.contributor.authorTorra Porras, Salvador
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.date.accessioned2020-07-09T11:33:55Z
dc.date.available2020-07-09T11:33:55Z
dc.date.issued2017-07-06
dc.identifier.citationClaveria, O.; Monte, E.; Torra Porras, S. "Regional tourism demand forecasting with machine learning models: Gaussian process regression vs. neural network models in a multiple-input multiple-output setting”". 2017.
dc.identifier.urihttp://hdl.handle.net/2117/192729
dc.descriptionWorking paper
dc.description.abstractThis study presents a multiple-input multiple-output (MIMO) approach for multi-step-ahead time series prediction with a Gaussian process regression (GPR) model. We assess the forecasting performance of the GPR model with respect to several neural network architectures. The MIMO setting allows modelling the cross-correlations between all regions simultaneously. We find that the radial basis function (RBF) network outperforms the GPR model, especially for long-term forecast horizons. As the memory of the models increases, the forecasting performance of the GPR improves, suggesting the convenience of designing a model selection criteria in order to estimate the optimal number of lags used for concatenation.
dc.format.extent26 p.
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
dc.subject.lcshEconomic forecasting
dc.subject.lcshNeural networks (Computer science)
dc.subject.otherTourism demand forecasting
dc.subject.otherMultiple-input multiple-output
dc.subject.otherGaussian process regression
dc.subject.otherRadial basis function
dc.titleRegional tourism demand forecasting with machine learning models: Gaussian process regression vs. neural network models in a multiple-input multiple-output setting”
dc.typeExternal research report
dc.subject.lemacPrevisió econòmica
dc.subject.lemacXarxes neuronals (Informàtica)
dc.contributor.groupUniversitat Politècnica de Catalunya. VEU - Grup de Tractament de la Parla
dc.relation.publisherversionhttp://www.ub.edu/irea/working_papers/2017/201701.pdf
dc.rights.accessOpen Access
local.identifier.drac28852242
dc.description.versionPreprint
local.citation.authorClaveria, O.; Monte, E.; Torra Porras, Salvador


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Attribution-NonCommercial-NoDerivs 3.0 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain