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“Tourism demand forecasting with different neural networks models”

Author

Listed:
  • Oscar Claveria

    (Faculty of Economics, University of Barcelona)

  • Enric Monte

    (Department of Signal Theory and Communications, Polytechnic University of Catalunya (UPC))

  • Salvador Torra

    (Faculty of Economics, University of Barcelona)

Abstract

This paper aims to compare the performance of different Artificial Neural Networks techniques for tourist demand forecasting. We test the forecasting accuracy of three different types of architectures: a multi-layer perceptron, a radial basis function and an Elman network. We also evaluate the effect of the memory by repeating the experiment assuming different topologies regarding the number of lags introduced. We used tourist arrivals from all the different countries of origin to Catalonia from 2001 to 2012. We find that multi-layer perceptron and radial basis function models outperform Elman networks, being the radial basis function architecture the one providing the best forecasts when no additional lags are incorporated. These results indicate the potential existence of instabilities when using dynamic networks for forecasting purposes. We also find that for higher memories, the forecasting performance obtained for longer horizons improves, suggesting the importance of increasing the dimensionality for long term forecasting.

Suggested Citation

  • Oscar Claveria & Enric Monte & Salvador Torra, 2013. "“Tourism demand forecasting with different neural networks models”," AQR Working Papers 201313, University of Barcelona, Regional Quantitative Analysis Group, revised Nov 2013.
    • Handle: RePEc:aqr:wpaper:201313
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    File URL: http://www.ub.edu/irea/working_papers/2013/201321.pdf
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    References listed on IDEAS

    as
    1. Witt, Stephen F. & Witt, Christine A., 1995. "Forecasting tourism demand: A review of empirical research," International Journal of Forecasting, Elsevier, vol. 11(3), pages 447-475, September.
    2. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    3. Claveria, Oscar & Torra, Salvador, 2014. "Forecasting tourism demand to Catalonia: Neural networks vs. time series models," Economic Modelling, Elsevier, vol. 36(C), pages 220-228.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Self-organizing map analysis of agents’ expectations. Different patterns of anticipation of the 2008 financial crisis”," AQR Working Papers 201508, University of Barcelona, Regional Quantitative Analysis Group, revised Mar 2015.
    2. İhsan Erdem Kayral & Tuğba Sarı & Nisa Şansel Tandoğan Aktepe, 2023. "Forecasting the Tourist Arrival Volumes and Tourism Income with Combined ANN Architecture in the Post COVID-19 Period: The Case of Turkey," Sustainability, MDPI, vol. 15(22), pages 1-20, November.
    3. Rendell E. de Kort, 2017. "Forecasting tourism demand through search queries and machine learning," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Big Data, volume 44, Bank for International Settlements.
    4. Dr. Murat çuhadar & Iclal Cogurcu & Ceyda Kukrer, 2014. "Modelling and Forecasting Cruise Tourism Demand to Izmir by Different Artificial Neural Network Architectures," International Journal of Business and Social Research, MIR Center for Socio-Economic Research, vol. 4(3), pages 12-28, March.
    5. Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Regional Forecasting with Support Vector Regressions: The Case of Spain”," IREA Working Papers 201507, University of Barcelona, Research Institute of Applied Economics, revised Jan 2015.

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    More about this item

    Keywords

    tourism demand; forecasting; artificial neural networks; multi-layer perceptron; radial basis function; Elman networks; Catalonia. JEL classification: L83; C53; C45; R11;
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