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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.accessioned2020-07-09T11:29:21Z
dc.date.available2020-07-09T11:29:21Z
dc.date.issued2015-07-09
dc.identifier.citationClaveria, O.; Monte, E.; Torra Porras, S. "Multiple-input multiple-output vs. single-input single-output neural network forecasting". 2015.
dc.identifier.urihttp://hdl.handle.net/2117/192726
dc.descriptionWorking Papers
dc.description.abstractThis study attempts to improve the forecasting accuracy of tourism demand by using the existing common trends in tourist arrivals form all visitor markets to a specific destination in a multiple-input multiple-output (MIMO) structure. While most tourism forecasting research focuses on univariate methods, we compare the performance of three different Artificial Neural Networks in a multivariate setting that takes into account the correlations in the evolution of inbound international tourism demand to Catalonia (Spain). We find that the MIMO approach does not outperform the forecasting accuracy of the networks when applied country by country, but it significantly improves the forecasting performance for total tourist arrivals. When comparing the forecast accuracy of the different models, we find that radial basis function networks outperform multilayer-perceptron and Elman networks.
dc.format.extent28 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.lcshArtificial intelligence
dc.subject.otherTourism demand
dc.subject.otherMultiple-input multiple-output
dc.subject.otherRadial basis function networks
dc.subject.otherMultilayer-perceptron
dc.subject.otherElman networks
dc.titleMultiple-input multiple-output vs. single-input single-output neural network forecasting
dc.typeExternal research report
dc.subject.lemacPrevisió econòmica
dc.subject.lemacIntel·ligència artificial
dc.contributor.groupUniversitat Politècnica de Catalunya. VEU - Grup de Tractament de la Parla
dc.relation.publisherversionhttp://www.ub.edu/irea/working_papers/2015/201502.pdf
dc.rights.accessOpen Access
local.identifier.drac28852267
dc.description.versionPreprint
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/6PN/TEC2012-38939-C03-02
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