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Regional tourism demand forecasting with machine learning models: Gaussian process regression vs. neural network models in a multiple-input multiple-output setting”
dc.contributor.author | Claveria González, 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 | 2020-07-09T11:33:55Z |
dc.date.available | 2020-07-09T11:33:55Z |
dc.date.issued | 2017-07-06 |
dc.identifier.citation | Claveria, 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.uri | http://hdl.handle.net/2117/192729 |
dc.description | Working paper |
dc.description.abstract | This 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.extent | 26 p. |
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::Matemàtiques i estadística::Estadística matemàtica |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
dc.subject.lcsh | Economic forecasting |
dc.subject.lcsh | Neural networks (Computer science) |
dc.subject.other | Tourism demand forecasting |
dc.subject.other | Multiple-input multiple-output |
dc.subject.other | Gaussian process regression |
dc.subject.other | Radial basis function |
dc.title | Regional tourism demand forecasting with machine learning models: Gaussian process regression vs. neural network models in a multiple-input multiple-output setting” |
dc.type | External research report |
dc.subject.lemac | Previsió econòmica |
dc.subject.lemac | Xarxes neuronals (Informàtica) |
dc.contributor.group | Universitat Politècnica de Catalunya. VEU - Grup de Tractament de la Parla |
dc.relation.publisherversion | http://www.ub.edu/irea/working_papers/2017/201701.pdf |
dc.rights.access | Open Access |
local.identifier.drac | 28852242 |
dc.description.version | Preprint |
local.citation.author | Claveria, O.; Monte, E.; Torra Porras, Salvador |
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