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Forecasting Accuracy Evaluation of Tourist Arrivals.

Hassani, H., Silva, E., Antonakakis, N., Filis, G. and Gupta, R., 2017. Forecasting Accuracy Evaluation of Tourist Arrivals. Annals of Tourism Research, 63 (March), 112-127.

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ATR_post-script.pdf - Accepted Version
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DOI: 10.1016/j.annals.2017.01.008


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.

Item Type:Article
Uncontrolled Keywords:Tourist arrivals; Forecasting; Singular Spectrum Analysis; Time Series Analysis.
Group:Bournemouth University Business School
ID Code:26275
Deposited By: Symplectic RT2
Deposited On:17 Jan 2017 12:29
Last Modified:14 Mar 2022 14:02


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