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“Regional Forecasting with Support Vector Regressions: The Case of Spain”

Author

Listed:
  • Oscar Claveria

    (Department of Econometrics. University of Barcelona)

  • Enric Monte

    (Department of Signal Theory and Communications. Polytechnic University of Catalunya.)

  • Salvador Torra

    (Department of Econometrics & Riskcenter-IREA. Universitat de Barcelona)

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.

Suggested Citation

  • Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Regional Forecasting with Support Vector Regressions: The Case of Spain”," AQR Working Papers 201506, University of Barcelona, Regional Quantitative Analysis Group, revised Jan 2015.
    • Handle: RePEc:aqr:wpaper:201506
    as

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    References listed on IDEAS

    as
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    5. Oscar Claveria & Enric Monte & Salvador Torra, 2013. "“Tourism demand forecasting with different neural networks models”," IREA Working Papers 201321, University of Barcelona, Research Institute of Applied Economics, revised Nov 2013.
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Forecasting; support vector regressions; artificial neural networks; tourism demand; Spain JEL classification: C02; C22; C45; C63; E27; R11;
    All these keywords.

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