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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.accessioned2020-07-09T14:01:06Z
dc.date.available2020-07-09T14:01:06Z
dc.date.issued2018-07-03
dc.identifier.citationClaveria, O.; Monte, E.; Torra Porras, S. "A regional perspective on the accuracy of machine learning forecasts of tourism demand based on data characteristics". 2018.
dc.identifier.urihttp://hdl.handle.net/2117/192753
dc.descriptionWorking paper
dc.description.abstractIn this work we assess the role of data characteristics in the accuracy of machine learning (ML) tourism forecasts from a spatial perspective. First, we apply a seasonal-trend decomposition procedure based on non-parametric regression to isolate the different components of the time series of international tourism demand to all Spanish regions. This approach allows us to compute a set of measures to describe the features of the data. Second, we analyse the performance of several ML models in a recursive multiple-step-ahead forecasting experiment. In a third step, we rank all seventeen regions according to their characteristics and the obtained forecasting performance, and use the rankings as the input for a multivariate analysis to evaluate the interactions between time series features and the accuracy of the predictions. By means of dimensionality reduction techniques we summarise all the information into two components and project all Spanish regions into perceptual maps. We find that entropy and dispersion show a negative relation with accuracy, while the effect of other data characteristics on forecast accuracy is heavily dependent on the forecast horizon.
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::Aprenentatge automàtic
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
dc.subject.lcshMachine learning
dc.subject.lcshEconomic forecasting
dc.subject.otherMachine learning
dc.subject.otherTourism forecasts
dc.subject.otherRecursive multiple-step-ahead forecasting
dc.titleA regional perspective on the accuracy of machine learning forecasts of tourism demand based on data characteristics
dc.typeExternal research report
dc.subject.lemacAprenentatge automàtic
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/2018/201805.pdf
dc.rights.accessOpen Access
local.identifier.drac28852034
dc.description.versionPreprint
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/1PE/ECO2016-75805-R
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/1PE/TEC2015-69266-P
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