Clavería González, ÓscarMonte Moreno, EnricTorra Porras, Salvador2015-02-042015-02-0420152014-1254https://hdl.handle.net/2445/62346This study attempts to assess the forecasting accuracy of Support Vector Regression (SVR) with regard to other Artificial Intelligence techniques based on statistical learning. We use two different neural networks and three SVR models that differ by the type of kernel used. We focus on international tourism demand to all seventeen regions of Spain. The SVR with a Gaussian kernel shows the best forecasting performance. The best predictions are obtained for longer forecast horizons, which suggest the suitability of machine learning techniques for medium and long term forecasting.40 p.application/pdfengcc-by-nc-nd, (c) Clavería et al., 2015http://creativecommons.org/licenses/by-nc-nd/3.0/Anàlisi de regressióPrevisió econòmicaPolítica turísticaDesenvolupament econòmicXarxes neuronals (Informàtica)Transmissió de dadesRegression analysisEconomic forecastingPolitics of tourismEconomic developmentNeural networks (Computer science)Data transmission systemsinfo:eu-repo/semantics/workingPaper2015-02-04info:eu-repo/semantics/openAccess