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Visitor arrivals forecasts amid COVID-19: A perspective from the Africa team
University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0003-0211-5218
School of Economic Sciences and Tourism Research in Economic Environs and Society (TREES), North-West University, Potchefstroom, South Africa.
Department of Economic and Social Sciences, University La Réunion, Saint-Denis, Reunion.
Department of Economics, Business and Statistics (SEAS), University of Palermo, Italy.
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2021 (English)In: Annals of Tourism Research, ISSN 0160-7383, E-ISSN 1873-7722, Vol. 88, article id 103197Article in journal (Refereed) Published
Abstract [en]

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%.

Place, publisher, year, edition, pages
Elsevier, 2021. Vol. 88, article id 103197
Keywords [en]
COVID-19, Forecasting, Hierarchical forecasts, Scenario forecasting, Visitor arrivals, competition (economics), forecasting method, hierarchical system, tourist destination, Africa
National Category
Peace and Conflict Studies Other Social Sciences not elsewhere specified Media and Communication Studies Public Health, Global Health and Social Medicine Economic Geography Other Geographic Studies
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
URN: urn:nbn:se:his:diva-19609DOI: 10.1016/j.annals.2021.103197ISI: 000652517500032Scopus ID: 2-s2.0-85103139988OAI: oai:DiVA.org:his-19609DiVA, id: diva2:1543953
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 © 2021 Elsevier Ltd

Available from: 2021-04-13 Created: 2021-04-13 Last updated: 2025-03-04Bibliographically approved

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Kourentzes, Nikolaos

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