Fotiadis Anestis, Polyzos Stathis, Huan Tzung-Cheng T C
College of Business, Zayed University, P.O. Box 144534, Abu Dhabi, United Arab Emirates.
Tainan University of Technology, No. 529, Zhongzheng Rd., Yongkang Dist., Tainan City 71002, Taiwan.
Ann Tour Res. 2021 Mar;87:103117. doi: 10.1016/j.annals.2020.103117. Epub 2020 Dec 13.
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.
本文旨在根据新冠疫情,生成国际旅游需求预测的不同情景。通过采用两种不同的方法(长短期记忆神经网络和广义相加模型),基于近期危机,我们能够计算出未来12个月国际游客到访量的预期降幅。我们使用滚动窗口测试策略来计算准确性指标,并表明尽管所有模型的准确性相当,但根据训练数据集得出的预测差异很大,这一发现应引起研究人员的警觉。我们的结果表明,游客到访量的降幅可能在30.8%至76.3%之间,并且至少会持续到2021年6月。