Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/90563
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dc.contributorSchool of Hotel and Tourism Managementen_US
dc.creatorLi, Xen_US
dc.creatorLi, Hen_US
dc.creatorPan, Ben_US
dc.creatorLaw, Ren_US
dc.date.accessioned2021-07-22T05:35:28Z-
dc.date.available2021-07-22T05:35:28Z-
dc.identifier.issn0047-2875en_US
dc.identifier.urihttp://hdl.handle.net/10397/90563-
dc.language.isoenen_US
dc.publisherSAGE Publicationsen_US
dc.rightsThis 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: 10.1177/0047287520934871en_US
dc.subjectFeature selectionen_US
dc.subjectHotel occupancyen_US
dc.subjectMachine learningen_US
dc.subjectSearch query dataen_US
dc.subjectTourism forecastingen_US
dc.titleMachine learning in internet search query selection for tourism forecastingen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1213en_US
dc.identifier.epage1231en_US
dc.identifier.volume60en_US
dc.identifier.issue6en_US
dc.identifier.doi10.1177/0047287520934871en_US
dcterms.abstractPrior 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.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationJournal of travel research, 1 July 2021, v. 60, no. 6, p. 1213-1231en_US
dcterms.isPartOfJournal of travel researchen_US
dcterms.issued2021-07-
dc.identifier.scopus2-s2.0-85087551597-
dc.identifier.eissn1552-6763en_US
dc.description.validate202107 bcvcen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumbera0984-n08-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextP0013971en_US
dc.description.pubStatusPublisheden_US
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