Forecasting accuracy evaluation of tourist arrivals

This source preferred by George Filis

Authors: Hassani, H., Silva, E.S., Antonakakis, N., Filis, G. and Gupta, R.

http://eprints.bournemouth.ac.uk/26275/

Journal: Annals of Tourism Research

Volume: 63

Pages: 112-127

ISSN: 1873-7722

This data was imported from Scopus:

Authors: Hassani, H., Silva, E.S., Antonakakis, N., Filis, G. and Gupta, R.

http://eprints.bournemouth.ac.uk/26275/

Journal: Annals of Tourism Research

Volume: 63

Pages: 112-127

ISSN: 0160-7383

DOI: 10.1016/j.annals.2017.01.008

© 2017 Elsevier Ltd 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.

This data was imported from Web of Science (Lite):

Authors: Hassani, H., Silva, E.S., Antonakakis, N., Filis, G. and Gupta, R.

http://eprints.bournemouth.ac.uk/26275/

Journal: ANNALS OF TOURISM RESEARCH

Volume: 63

Pages: 112-127

ISSN: 0160-7383

DOI: 10.1016/j.annals.2017.01.008

The data on this page was last updated at 04:42 on November 25, 2017.