Forecasting tourism demand of tourist attractions during the COVID-19 pandemic

Dilin Chen, Fenglan Sun and Zhixue Liao

Current Issues in Tourism, 2024, vol. 27, issue 3, 445-463

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.

Date: 2024
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DOI: 10.1080/13683500.2023.2165482

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