Can multi-source heterogeneous data improve the forecasting performance of tourist arrivals amid COVID-19? Mixed-data sampling approach
| dc.contributor.author | Wu, Jing | |
| dc.contributor.author | Li, Mingchen | |
| dc.contributor.author | Zhao, Erlong | |
| dc.contributor.author | Sun, Shaolong | |
| dc.contributor.author | Wang, Shouyang | |
| dc.date.accessioned | 2024-08-08T03:36:08Z | |
| dc.date.available | 2024-08-08T03:36:08Z | |
| dc.date.issued | 2023 | |
| dc.description | Tourism Management 98 (2023) | vi |
| dc.description.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 deĀ mand 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. | vi |
| dc.identifier.uri | https://thuvienso.hoasen.edu.vn/handle/123456789/15555 | |
| dc.language.iso | en | vi |
| dc.publisher | Elservier | vi |
| dc.subject | Tourism demand forecasting,Online news,Search query data,MIDAS,GDFM | vi |
| dc.title | Can multi-source heterogeneous data improve the forecasting performance of tourist arrivals amid COVID-19? Mixed-data sampling approach | vi |
| dc.type | Article | vi |
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