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Modelling and Forecasting Cruise Tourism Demand to Izmir by Different Artificial Neural Network Architectures

Author

Listed:
  • Dr. Murat çuhadar

    (Assistant professor, Egirdir Vocational School of Higher Education, Tourism&Hotel Administration Department, Süleyman Demirel University, Turkey.,)

  • Iclal Cogurcu

    (Faculty of Economic and Administrative Sciences, Department of Economics, Karamanoglu Mehmet Bey University, Turkey.)

  • Ceyda Kukrer

    (Faculty of Economic and Administrative Sciences, Department of Finance,Afyonkocatepe University, Turkey)

Abstract

Cruise ports emerged as an important sector for the economy of Turkey bordered on three sides by water. Forecasting cruise tourism demand ensures better planning, efficient preparation at the destination and it is the basis for elaboration of future plans. In the recent years, new techniques such as; artificial neural networks were employed for developing of the predictive models to estimate tourism demand. In this study, it is aimed to determine the forecasting method that provides the best performance when compared the forecast accuracy of Multi-Layer Perceptron (MLP), Radial Basis Function (RBF) and Generalized Regression neural network (GRNN) to estimate the monthly inbound cruise tourism demand to Izmir via the method giving best results. We used the total number of foreign cruise tourist arrivals as a measure of inbound cruise tourism demand and monthly cruise tourist arrivals to Izmir Cruise Port in the period of January 2005†December 2013. We reutilized to appropriate model. Experimental results showed that radial basis function (RBF) neural network outperforms multi-layer perceptron (MLP) and the generalised regression neural networks (GRNN) in terms of forecasting accuracy. By the means of the obtained RBF neural network model, it has been forecasted the monthly inbound cruise tourism demand to Izmir for the year 2014.

Suggested Citation

  • 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.
    • Handle: RePEc:mir:mirbus:v:4:y:2014:i:3:p:12-28
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    References listed on IDEAS

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    1. Chang-Jui Lin & Hsueh-Fang Chen & Tian-Shyug Lee, 2011. "Forecasting Tourism Demand Using Time Series, Artificial Neural Networks and Multivariate Adaptive Regression Splines:Evidence from Taiwan," International Journal of Business Administration, International Journal of Business Administration, Sciedu Press, vol. 2(2), pages 14-24, May.
    2. 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.
    3. Sen Cheong Kon & Lindsay W. Turner, 2005. "Neural Network Forecasting of Tourism Demand," Tourism Economics, , vol. 11(3), pages 301-328, September.
    4. Ivana Pavlić, 2013. "Cruise tourism demand forecasting - the case of Dubrovnik," Tourism and Hospitality Management, University of Rijeka, Faculty of Tourism and Hospitality Management, vol. 19(1), pages 125-142, May.
    5. Larry Dwyer & Peter Forsyth (ed.), 2006. "International Handbook on the Economics of Tourism," Books, Edward Elgar Publishing, number 2827.
    6. 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.
    7. Peter Wild & John Dearing, 2000. "Development of and prospects for cruising in Europe," Maritime Policy & Management, Taylor & Francis Journals, vol. 27(4), pages 315-333.
    8. C. Petropoulos & K. Nikolopoulos & A. Patelis & V. Assimakopoulos, 2005. "A technical analysis approach to tourism demand forecasting," Applied Economics Letters, Taylor & Francis Journals, vol. 12(6), pages 327-333.
    9. Zhang, G. Peter & Qi, Min, 2005. "Neural network forecasting for seasonal and trend time series," European Journal of Operational Research, Elsevier, vol. 160(2), pages 501-514, January.
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