The good, the bad and the ugly on COVID-19 tourism recovery

被引:242
作者
Fotiadis, Anestis [1 ]
Polyzos, Stathis [1 ]
Huan, Tzung-Cheng T. C. [2 ]
机构
[1] Zayed Univ, Coll Business, POB 144534, Abu Dhabi, U Arab Emirates
[2] Tainan Univ Technol, 529 Zhongzheng Rd, Tainan 71002, Taiwan
关键词
Coronavirus; Tourism demand; Deep learning; Generalized additive model; Pandemia; SARS;
D O I
10.1016/j.annals.2020.103117
中图分类号
F [经济];
学科分类号
02 ;
摘要
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. (C) 2020 Elsevier Ltd. All rights reserved.
引用
收藏
页数:14
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