Regional Forecasting with Support Vector Regressions: The Case of Spain

Oscar Claveria, Enric Monte () and Salvador Torra ()
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Enric Monte: Department of Signal Theory and Communications. Polytechnic University of Catalunya.
Salvador Torra: Department of Econometrics & Riskcenter-IREA. Universitat de Barcelona

No 201506, AQR Working Papers from University of Barcelona, Regional Quantitative Analysis Group

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-ore and nep-tur
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http://www.ub.edu/irea/working_papers/2015/201507.pdf (application/pdf)

Related works:
Working Paper: Regional Forecasting with Support Vector Regressions: The Case of Spain (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:aqr:wpaper:201506

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