Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/90563
DC Field | Value | Language |
---|---|---|
dc.contributor | School of Hotel and Tourism Management | en_US |
dc.creator | Li, X | en_US |
dc.creator | Li, H | en_US |
dc.creator | Pan, B | en_US |
dc.creator | Law, R | en_US |
dc.date.accessioned | 2021-07-22T05:35:28Z | - |
dc.date.available | 2021-07-22T05:35:28Z | - |
dc.identifier.issn | 0047-2875 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/90563 | - |
dc.language.iso | en | en_US |
dc.publisher | SAGE Publications | en_US |
dc.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 | en_US |
dc.subject | Feature selection | en_US |
dc.subject | Hotel occupancy | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Search query data | en_US |
dc.subject | Tourism forecasting | en_US |
dc.title | Machine learning in internet search query selection for tourism forecasting | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 1213 | en_US |
dc.identifier.epage | 1231 | en_US |
dc.identifier.volume | 60 | en_US |
dc.identifier.issue | 6 | en_US |
dc.identifier.doi | 10.1177/0047287520934871 | en_US |
dcterms.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. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Journal of travel research, 1 July 2021, v. 60, no. 6, p. 1213-1231 | en_US |
dcterms.isPartOf | Journal of travel research | en_US |
dcterms.issued | 2021-07 | - |
dc.identifier.scopus | 2-s2.0-85087551597 | - |
dc.identifier.eissn | 1552-6763 | en_US |
dc.description.validate | 202107 bcvc | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | a0984-n08 | - |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | P0013971 | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Li_Machine_Learning_Internet.pdf | Pre-Published version | 1.49 MB | Adobe PDF | View/Open |
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