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Can multi-source heterogeneous data improve the forecasting performance of tourist arrivals amid COVID-19? Mixed-data sampling approach by Jing Wu; Mingchen Li; Erlong Zhao; Shaolong Sun; Shouyang Wang is a Social Sciences article available to read on EtoBox.

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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.

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Author

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

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Social Sciences