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http://hdl.handle.net/10397/90563
Title: | Machine learning in internet search query selection for tourism forecasting | Authors: | Li, X Li, H Pan, B Law, R |
Issue Date: | Jul-2021 | Source: | Journal of travel research, 1 July 2021, v. 60, no. 6, p. 1213-1231 | Abstract: | Prior studies have shown that Internet search query data have great potential to improve tourism forecasting. As such, selecting the most relevant information from large amounts of search query data is crucial to enhancing forecasting accuracy and reducing overfitting; however, such feature selection methods have not been considered in the tourism forecasting literature. This study employs four machine learning–based feature selection methods to extract useful search query data and construct relevant econometric models. We examined the proposed methods based on monthly forecasting of tourist arrivals in Beijing, China, along with weekly forecasting of hotel occupancy in the city of Charleston, South Carolina, USA. Our findings indicate that the forecasting model with the selected search keywords outperformed the benchmark ARMAX model without feature selection in forecasting tourism demand and hotel occupancy. Therefore, machine learning methods can identify the most useful search query data to significantly improve forecasting accuracy in tourism and hospitality. | Keywords: | Feature selection Hotel occupancy Machine learning Search query data Tourism forecasting |
Publisher: | SAGE Publications | Journal: | Journal of travel research | ISSN: | 0047-2875 | EISSN: | 1552-6763 | DOI: | 10.1177/0047287520934871 | Rights: | This is the accepted version of the publication Li X, Li H, Pan B, Law R. Machine Learning in Internet Search Query Selection for Tourism Forecasting. Journal of Travel Research. 2021;60(6):1213-1231. Copyright © 2020 (The Author(s)). DOI: 10.1177/0047287520934871 |
Appears in Collections: | Journal/Magazine Article |
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Li_Machine_Learning_Internet.pdf | Pre-Published version | 1.49 MB | Adobe PDF | View/Open |
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