Forecasting accuracy evaluation of tourist arrivals

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Authors

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

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Journal ISSN

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Publisher

Elsevier

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.

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Keywords

Tourist arrivals, Forecasting, Singular spectrum analysis, Time series analysis

Sustainable Development Goals

Citation

Hassani, H, Silva, ES, Antonakakis, N, Filis, G & Gupta, R 2017, 'Forecasting accuracy evaluation of tourist arrivals', Annals of Tourism Research, vol. 63, pp. 112-127