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Regional Forecasting with Support Vector Regressions: The Case of Spain
Clavería González, Óscar; Monte Moreno, Enric; Torra Porras, Salvador
This 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.
Anàlisi de regressió
Previsió econòmica
Política turística
Desenvolupament econòmic
Xarxes neuronals (Informàtica)
Transmissió de dades
Regression analysis
Economic forecasting
Politics of tourism
Economic development
Neural networks (Computer science)
Data transmission systems
cc-by-nc-nd, (c) Clavería et al., 2015
http://creativecommons.org/licenses/by-nc-nd/3.0/
Documento de trabajo
Universitat de Barcelona. Institut de Recerca en Economia Aplicada Regional i Pública
         

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Clavería González, Óscar; Monte Moreno, Enric; Torra Porras, Salvador
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