Forecast daily tourist volumes during the epidemic period using COVID-19 data, search engine data and weather data

The COVID-19 epidemic has brought a devastating blow to the tourism industry. Affected by the epidemic sit-uation, 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 opera-tional risks.