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Forecasting tourist arrivals with machine learning and internet search index
Sun, Shaolong1,2,3; Wei, Yunjie1,4; Tsui, Kwok-Leung3; Wang, Shouyang1,2,4
2019-02-01
Source PublicationTOURISM MANAGEMENT
ISSN0261-5177
Volume70Pages:1-10
AbstractPrevious studies have shown that online data, such as search engine queries, is a new source of data that can be used to forecast tourism demand. In this study, we propose a forecasting framework that uses machine learning and internet search indexes to forecast tourist arrivals for popular destinations in China and compared its forecasting performance to the search results generated by Google and Baidu, respectively. This study verifies the Granger causality and co-integration relationship between internet search index and tourist arrivals of Beijing. Our experimental results suggest that compared with benchmark models, the proposed kernel extreme learning machine (KELM) models, which integrate tourist volume series with Baidu Index and Google Index, can improve the forecasting performance significantly in terms of both forecasting accuracy and robustness analysis.
KeywordTourism demand forecasting Kernel extreme learning machine Search query data Big data analytics Composite search index
DOI10.1016/j.tourman.2018.07.010
Language英语
Funding ProjectNational Natural Science Foundation of China[51505307] ; National Natural Science Foundation of China[11471275] ; General Research Fund[CityU 11216014] ; Research Grants Council of the Hong Kong Special Administrative Region, China[T32-101/15-R]
WOS Research AreaEnvironmental Sciences & Ecology ; Social Sciences - Other Topics ; Business & Economics
WOS SubjectEnvironmental Studies ; Hospitality, Leisure, Sport & Tourism ; Management
WOS IDWOS:000448096100001
PublisherELSEVIER SCI LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/31689
Collection系统科学研究所
Affiliation1.Chinese Acad Sci, Acad Math & Syst Sci, Zhongguancun East Rd 55, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Econ & Management, Beijing 100190, Peoples R China
3.City Univ Hong Kong, Dept Syst Engn & Engn Management, Tat Chee Ave, Kowloon, Hong Kong, Peoples R China
4.Chinese Acad Sci, Ctr Forecasting Sci, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Sun, Shaolong,Wei, Yunjie,Tsui, Kwok-Leung,et al. Forecasting tourist arrivals with machine learning and internet search index[J]. TOURISM MANAGEMENT,2019,70:1-10.
APA Sun, Shaolong,Wei, Yunjie,Tsui, Kwok-Leung,&Wang, Shouyang.(2019).Forecasting tourist arrivals with machine learning and internet search index.TOURISM MANAGEMENT,70,1-10.
MLA Sun, Shaolong,et al."Forecasting tourist arrivals with machine learning and internet search index".TOURISM MANAGEMENT 70(2019):1-10.
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