Regional Forecasting with Support Vector Regressions: The Case of Spain

Oscar Claveria, Enric Monte () and Salvador Torra ()
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Enric Monte: Polytechnic University of Catalunya
Salvador Torra: Faculty of Economics, University of Barcelona

No 201507, IREA Working Papers from University of Barcelona, Research Institute of Applied Economics

Abstract: 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.

Keywords: Forecasting; support vector regressions; artificial neural networks; tourism demand; Spain JEL classification: C02; C22; C45; C63; E27; R11 (search for similar items in EconPapers)
Pages: 40 pages
Date: 2015-01, Revised 2015-01
New Economics Papers: this item is included in nep-cmp, nep-for, nep-mac and nep-tur
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Working Paper: Regional Forecasting with Support Vector Regressions: The Case of Spain (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:ira:wpaper:201507

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