Hassani, HosseinSilva, Emmanuel SirimalAntonakakis, NikolaosFilis, GeorgeGupta, Rangan2017-03-062017-03Hassani, H, Silva, ES, Antonakakis, N, Filis, G & Gupta, R 2017, 'Forecasting accuracy evaluation of tourist arrivals', Annals of Tourism Research, vol. 63, pp. 112-1270160-738310.1016/j.annals.2017.01.008http://hdl.handle.net/2263/59275This 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.en© 2017 Elsevier Ltd. All rights reserved. Notice : this is the author’s version of a work that was accepted for publication in Annals of Tourism Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. A definitive version was subsequently published in Annals of Tourism Research, vol. 63, pp. 112-127, 2017. doi : 10.1016/j.annals.2017.01.008.Tourist arrivalsForecastingSingular spectrum analysisTime series analysisPostprint Article