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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.accessioned2020-07-09T11:31:54Z
dc.date.available2020-07-09T11:31:54Z
dc.date.issued2015-07-02
dc.identifier.citationClaveria, O.; Monte, E.; Torra Porras, S. "Effects of removing the trend and the seasonal component on the forecasting performance of artificial neural network techniques". 2015.
dc.identifier.urihttp://hdl.handle.net/2117/192727
dc.descriptionWorking paper
dc.description.abstractThis study aims to analyze the effects of data pre-processing on the performance of forecasting based on neural network models. We use three different Artificial Neural Networks techniques to forecast tourist demand: a multi-layer perceptron, a radial basis function and an Elman neural network. The structure of the networks is based on a multiple-input multiple-output setting (i.e. all countries are forecasted simultaneously). We use official statistical data of inbound international tourism demand to Catalonia (Spain) and compare the forecasting accuracy of four processing methods for the input vector of the networks: levels, growth rates, seasonally adjusted levels and seasonally adjusted growth rates. When comparing the forecasting accuracy of the different inputs for each visitor market and for different forecasting horizons, we obtain significantly better forecasts with levels than with growth rates. We also find that seasonally adjusted series significantly improve the forecasting performance of the networks, which hints at the significance of deseasonalizing the time series when using neural networks with forecasting purposes. These results reveal that, when using seasonal data, neural networks performance can be significantly improved by working directly with seasonally adjusted levels.
dc.format.extent16 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::Informàtica::Intel·ligència artificial
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
dc.subject.lcshNeural networks (Computer science)
dc.subject.lcshEconomic forecasting
dc.subject.otherArtificial neural network
dc.subject.otherTourist forecasting
dc.subject.otherElman neural networks
dc.subject.otherBack propagation
dc.subject.otherRadial basis
dc.subject.otherMultilayer neural network
dc.titleEffects of removing the trend and the seasonal component on the forecasting performance of artificial neural network techniques
dc.typeExternal research report
dc.subject.lemacXarxes neuronals (Informàtica)
dc.subject.lemacPrevisió econòmica
dc.contributor.groupUniversitat Politècnica de Catalunya. VEU - Grup de Tractament de la Parla
dc.relation.publisherversionhttp://www.ub.edu/irea/working_papers/2015/201503.pdf
dc.rights.accessOpen Access
local.identifier.drac28852256
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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Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain