Visitor arrivals forecasts amid COVID-19: A perspective from the Africa team

Nikolaos Kourentzes; Andrea Saayman; Philippe Jean-Pierre; Davide Provenzano; Mondher Sahli; Neelu Seetaram; Serena Volo

doi:10.1016/j.annals.2021.103197

Visitor arrivals forecasts amid COVID-19: A perspective from the Africa team

Ann Tour Res. 2021 May:88:103197. doi: 10.1016/j.annals.2021.103197. Epub 2021 Mar 20.

Authors

Nikolaos Kourentzes¹, Andrea Saayman², Philippe Jean-Pierre³, Davide Provenzano⁴, Mondher Sahli⁵, Neelu Seetaram⁶, Serena Volo⁷

Affiliations

¹ Skövde Artificial Intelligence Lab, Skövde University, 541 28 Skövde, Sweden.
² School of Economic Sciences and Tourism Research in Economic Environs and Society (TREES), North-West University, Potchefstroom 2520, South Africa.
³ Department of Economic and Social Sciences, University La Réunion, 97744 Saint Denis Cedex 9, Saint-Denis, Réunion.
⁴ Department of Economics, Business and Statistics (SEAS), University of Palermo, 90128 Palermo, Italy.
⁵ Wellington School of Business and Government, Victoria University of Wellington, PO Box 600, Wellington, New Zealand.
⁶ School of Events, Tourism and Hospitality Management, Leeds Beckett University, Headingley Campus, Leeds LS6 3QN, UK.
⁷ Economics and Management and Competence Centre in Tourism Management and Tourism Economics (TOMTE), Free University of Bozen-Bolzano, 39031 Brunico, Italy.

Abstract

COVID-19 disrupted international tourism worldwide, subsequently presenting forecasters with a challenging conundrum. In this competition, we predict international arrivals for 20 destinations in two phases: (i) Ex post forecasts pre-COVID; (ii) Ex ante forecasts during and after the pandemic up to end 2021. Our results show that univariate combined with cross-sectional hierarchical forecasting techniques (THieF-ETS) outperform multivariate models pre-COVID. Scenarios were developed based on judgemental adjustment of the THieF-ETS baseline forecasts. Analysts provided a regional view on the most likely path to normal, based on country-specific regulations, macroeconomic conditions, seasonal factors and vaccine development. Results show an average recovery of 58% compared to 2019 tourist arrivals in the 20 destinations under the medium scenario; severe, it is 34% and mild, 80%.

Keywords: COVID-19; Forecasting; Hierarchical forecasts; Scenario forecasting; Visitor arrivals.