ABSTRACT
Due to the COVID-19 outbreak, forecasting the tourism demand of tourist attractions is facing unprecedented difficulties given the lack of understanding about the pandemic impacts and the unavailability of post-pandemic data for generating forecasts. In this study, two strategies are proposed to improve forecasting performance and address the above difficulties. First, a novel COVID-19 impact indicator is built to reflect the impacts of the pandemic on tourism demand. Second, an effective forecast aggregation algorithm is developed to efficiently generate forecasts despite limited post-pandemic data availability. To validate the effectiveness of these strategies, an empirical study using real data from a tourist attraction is conducted, and results demonstrate that these strategies improve the overall forecast performance, including forecast accuracy and stability.
Disclosure statement
No potential conflict of interest was reported by the author(s).