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Forecasting Tourist Arrivals to Balearic Islands Using Genetic Programming

Author

Listed:
  • Marcos Álvarez Díaz
  • Josep Mateu Sbert
  • Jaume Rosselló Nadal
Abstract
Traditionally, univariate time-series models have largely dominated forecasting for international tourism demand. In this paper, the ability of a Genetic Programming (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, but it is practically unknown into tourism forecasters. 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 Álvarez Díaz & Josep Mateu Sbert & Jaume Rosselló Nadal, 2007. "Forecasting Tourist Arrivals to Balearic Islands Using Genetic Programming," CRE Working Papers (Documents de treball del CRE) 2007/02, Centre de Recerca Econòmica (UIB ·"Sa Nostra"), revised Jan 2007.
    • Handle: RePEc:pdm:wpaper:2007/02
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    File URL: http://www.cre.sanostra.es/internet/cre.nsf/pernomcurt/DT_adjunt/$FILE/dt2007_2.pdf
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    Citations

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    Cited by:

    1. Tairan Zhang & Zhenji Zhang & Gang Xue, 2024. "Mitigating the disturbances of events on tourism demand forecasting," Annals of Operations Research, Springer, vol. 342(1), pages 1019-1040, November.
    2. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "“Tracking economic growth by evolving expectations via genetic programming: A two-step approach”," AQR Working Papers 201801, University of Barcelona, Regional Quantitative Analysis Group, revised Jan 2018.
    3. 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.
    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.
    6. 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.

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