Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/121328
Full metadata record
DC FieldValueLanguage
dc.contributor.authorClavería González, Óscar-
dc.contributor.authorMonte Moreno, Enric-
dc.contributor.authorTorra Porras, Salvador-
dc.date.accessioned2018-04-06T08:40:00Z-
dc.date.available2018-04-06T08:40:00Z-
dc.date.issued2018-
dc.identifier.issn1136-8365-
dc.identifier.urihttp://hdl.handle.net/2445/121328-
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.format.extent24 p.-
dc.format.mimetypeapplication/pdf-
dc.language.isoeng-
dc.publisherUniversitat de Barcelona. Facultat d'Economia i Empresa-
dc.relation.isformatofReproducció del document publicat a: http://www.ub.edu/irea/working_papers/2018/201805.pdf-
dc.relation.ispartofIREA – Working Papers, 2018, IR18/05-
dc.relation.ispartofAQR – Working Papers, 2018, AQR18/02-
dc.relation.ispartofseries[WP E-IR18/05]-
dc.relation.ispartofseries[WP E-AQR18/02]-
dc.rightscc-by-nc-nd, (c) Clavería González et al., 2018-
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/-
dc.sourceDocuments de treball (Institut de Recerca en Economia Aplicada Regional i Pública (IREA))-
dc.subject.classificationEstadística no paramètrica-
dc.subject.classificationAnàlisi de sèries temporals-
dc.subject.classificationPolítica regional-
dc.subject.classificationPolítica turística-
dc.subject.otherNonparametric statistics-
dc.subject.otherTime-series analysis-
dc.subject.otherEconomic zoning-
dc.subject.otherPolitics of tourism-
dc.titleA regional perspective on the accuracy of machine learning forecasts of tourism demand based on data characteristics-
dc.typeinfo:eu-repo/semantics/workingPaper-
dc.date.updated2018-04-06T08:40:00Z-
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess-
Appears in Collections:AQR (Grup d’Anàlisi Quantitativa Regional) – Working Papers
Documents de treball (Institut de Recerca en Economia Aplicada Regional i Pública (IREA))

Files in This Item:
File Description SizeFormat 
IR18-005_Claveria+Monte+Torra.pdf1.81 MBAdobe PDFView/Open


This item is licensed under a Creative Commons License Creative Commons