Zhang Chuan, Tian Yu-Xin
School of Business Administration, Northeastern University, Shenyang 110169, China.
Expert Syst Appl. 2022 Dec 30;210:118505. doi: 10.1016/j.eswa.2022.118505. Epub 2022 Aug 12.
The COVID-19 epidemic has brought a devastating blow to the tourism industry. Affected by the epidemic situation, the change of tourism volume of scenic spots is very unstable. Therefore, forecasting tourist volume in the context of COVID-19 epidemic is a new and challenging problem. In response, a novel multivariate time series forecasting framework based on variational mode decomposition (VMD) and gated recurrent unit network (GRU), i.e., VMD-GRU, is proposed to forecast daily tourist volumes during the epidemic. It takes the lead in using COVID-19 data, search traffic data and weather data. Through sufficient experiments and comparisons, the superiority of the approach is illustrated, and the predictive power of the above three types of data, especially the COVID-19 data, is revealed. Accurate forecast results from the method can help relevant government officials and tourism practitioners to better adjust tourism resources, cooperate with anti-epidemic work and reduce operational risks.
新冠疫情给旅游业带来了毁灭性打击。受疫情影响，景区旅游量变化极不稳定。因此，在新冠疫情背景下预测旅游量是一个全新且具有挑战性的问题。对此，提出了一种基于变分模态分解（VMD）和门控循环单元网络（GRU）的新型多元时间序列预测框架，即VMD - GRU，用于预测疫情期间的每日旅游量。该框架率先使用新冠疫情数据、搜索流量数据和天气数据。通过充分的实验和比较，阐明了该方法的优越性，并揭示了上述三种类型数据，尤其是新冠疫情数据的预测能力。该方法得出的准确预测结果有助于相关政府官员和旅游从业者更好地调配旅游资源、配合抗疫工作并降低运营风险。
