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Forecasting tourist arrivals to Balearic Islands using genetic programming

Author

Listed:
  • Marcos Alvarez-Diaz
  • Josep Mateu-Sbert
  • Jaume Rossello-Nadal

Abstract

Traditionally, univariate time-series models have largely dominated forecasting for international tourism demand. In this paper, the ability of a genetic program (GP) to predict monthly tourist arrivals from UK and Germany to Balearic Islands, Spain is explored. GP has already been employed satisfactorily in different scientific areas, including economics. The technique shows different advantages regarding to other forecasting methods. Firstly, it does not assume a priori a rigid functional form of the model. Secondly, it is more robust and easy-to-use than other non-parametric methods. Finally, it provides explicitly a mathematical equation which allows a simple ad hoc interpretation of the results. Comparing the performance of the proposed technique against other method commonly used in tourism forecasting (no-change model, moving average and ARIMA), the empirical results reveal that GP can be a valuable tool in this field.

Suggested Citation

  • Marcos Alvarez-Diaz & Josep Mateu-Sbert & Jaume Rossello-Nadal, 2009. "Forecasting tourist arrivals to Balearic Islands using genetic programming," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 1(1), pages 64-75.
  • Handle: RePEc:ids:ijcome:v:1:y:2009:i:1:p:64-75
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    Cited by:

    1. Yang, Xin & Pan, Bing & Evans, James A. & Lv, Benfu, 2015. "Forecasting Chinese tourist volume with search engine data," Tourism Management, Elsevier, vol. 46(C), pages 386-397.
    2. Sorić, Petar & Lolić, Ivana & Claveria, Oscar & Monte, Enric & Torra, Salvador, 2019. "Unemployment expectations: A socio-demographic analysis of the effect of news," Labour Economics, Elsevier, vol. 60(C), pages 64-74.
    3. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "Tracking economic growth by evolving expectations via genetic programming: A two-step approach," Working Papers XREAP2018-4, Xarxa de Referència en Economia Aplicada (XREAP), revised Oct 2018.
    4. Law, Rob & Li, Gang & Fong, Davis Ka Chio & Han, Xin, 2019. "Tourism demand forecasting: A deep learning approach," Annals of Tourism Research, Elsevier, vol. 75(C), pages 410-423.
    5. Oscar Claveria & Enric Monte & Salvador Torra, 2019. "Evolutionary Computation for Macroeconomic Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 833-849, February.

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