Can multi-source heterogeneous data improve the forecasting performance of tourist arrivals amid COVID-19? Mixed-data sampling approach

Jing Wu; Mingchen Li; Erlong Zhao; Shaolong Sun; Shouyang Wang

doi:10.1016/j.tourman.2023.104759

Can multi-source heterogeneous data improve the forecasting performance of tourist arrivals amid COVID-19? Mixed-data sampling approach

Tour Manag. 2023 Oct:98:104759. doi: 10.1016/j.tourman.2023.104759. Epub 2023 Apr 3.

Authors

Jing Wu¹, Mingchen Li^{2

3}, Erlong Zhao¹, Shaolong Sun¹, Shouyang Wang^{2

3

4}

Affiliations

¹ School of Management, Xi'an Jiaotong University, Xi'an, 710049, China.
² Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China.
³ School of Economics and Management, University of Chinese Academy of Sciences, Beijing, 100190, China.
⁴ School of Entrepreneurship and Management, ShanghaiTech University, Shanghai, 201210, China.

Abstract

The coronavirus disease (COVID-19) pandemic has already caused enormous damage to the global economy and various industries worldwide, especially the tourism industry. In the post-pandemic era, accurate tourism demand recovery forecasting is a vital requirement for a thriving tourism industry. Therefore, this study mainly focuses on forecasting tourist arrivals from mainland China to Hong Kong. A new direction in tourism demand recovery forecasting employs multi-source heterogeneous data comprising economy-related variables, search query data, and online news data to motivate the tourism destination forecasting system. The experimental results confirm that incorporating multi-source heterogeneous data can substantially strengthen the forecasting accuracy. Specifically, mixed data sampling (MIDAS) models with different data frequencies outperformed the benchmark models.

Keywords: GDFM; MIDAS; Online news; Search query data; Tourism demand forecasting.