The good, the bad and the ugly on COVID-19 tourism recovery
Document Type
Article
Source of Publication
Annals of Tourism Research
Publication Date
3-1-2021
Abstract
© 2020 Elsevier Ltd This paper is to produce different scenarios in forecasts for international tourism demand, in light of the COVID-19 pandemic. By implementing two distinct methodologies (the Long Short Term Memory neural network and the Generalized Additive Model), based on recent crises, we are able to calculate the expected drop in the international tourist arrivals for the next 12 months. We use a rolling-window testing strategy to calculate accuracy metrics and show that even though all models have comparable accuracy, the forecasts produced vary significantly according to the training data set, a finding that should be alarming to researchers. Our results indicate that the drop in tourist arrivals can range between 30.8% and 76.3% and will persist at least until June 2021.
DOI Link
ISSN
Publisher
Elsevier BV
Volume
87
First Page
103117
Disciplines
Tourism and Travel
Keywords
Coronavirus, Deep learning, Generalized additive model, Pandemia, Tourism demand
Scopus ID
Recommended Citation
Fotiadis, Anestis; Polyzos, Stathis; and Huan, Tzung Cheng T.C., "The good, the bad and the ugly on COVID-19 tourism recovery" (2021). All Works. 3454.
https://zuscholars.zu.ac.ae/works/3454
Indexed in Scopus
yes
Open Access
yes
Open Access Type
Green: A manuscript of this publication is openly available in a repository