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Forecasting accuracy evaluation of tourist arrivals

Author

Listed:
  • Hassani, Hossein
  • Silva, Emmanuel Sirimal
  • Antonakakis, Nikolaos
  • Filis, George
  • Gupta, Rangan

Abstract

This paper evaluates the use of several parametric and nonparametric forecasting techniques for predicting tourism demand in selected European countries. We find that no single model can provide the best forecasts for any of the countries in the short-, medium- and long-run. The results, which are tested for statistical significance, enable forecasters to choose the most suitable model (from those evaluated here) based on the country and horizon for forecasting tourism demand. Should a single model be of interest, then, across all selected countries and horizons the Recurrent Singular Spectrum Analysis model is found to be the most efficient based on lowest overall forecasting error. Neural Networks and ARFIMA are found to be the worst performing models.

Suggested Citation

  • Hassani, Hossein & Silva, Emmanuel Sirimal & Antonakakis, Nikolaos & Filis, George & Gupta, Rangan, 2017. "Forecasting accuracy evaluation of tourist arrivals," Annals of Tourism Research, Elsevier, vol. 63(C), pages 112-127.
    • Handle: RePEc:eee:anture:v:63:y:2017:i:c:p:112-127
    DOI: 10.1016/j.annals.2017.01.008
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    References listed on IDEAS

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