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Tourism demand forecasting: A deep learning approach
Law,Rob1; Li,Gang2; Fong,Davis Ka Chio3; Han,Xin4
2019-03-01
Source PublicationAnnals of Tourism Research
ISSN01607383
Volume75Pages:410-423
Abstract

Traditional tourism demand forecasting models may face challenges when massive amounts of search intensity indices are adopted as tourism demand indicators. Using a deep learning approach, this research studied the framework in forecasting monthly Macau tourist arrival volumes. The empirical results demonstrated that the deep learning approach significantly outperforms support vector regression and artificial neural network models. Moreover, the construction and identification of highly relevant features from the proposed deep network architecture provide practitioners with a means of understanding the relationships between various tourist demand forecasting factors and tourist arrival volumes. This article also launches the Annals of Tourism Research Curated Collection on Tourism Demand Forecasting, a special selection of research in this field

KeywordAttention Mechanism Deep Learning Feature Engineering Lag Order Long-short-term-memory Tourism Demand Forecasting
DOI10.1016/j.annals.2019.01.014
URLView the original
Indexed BySSCI
Language英语
WOS Research AreaSocial Sciences - Other Topics ; Sociology
WOS SubjectHospitality, Leisure, Sport & Tourism ; Sociology
WOS IDWOS:000474679500030
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Citation statistics
Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF INTEGRATED RESORT AND TOURISM MANAGEMENT
Corresponding AuthorLaw,Rob
Affiliation1.School of Hotel & Tourism Management,The Hong Kong Polytechnic University,,Kowloon,Hong Kong
2.School of Information Technology,Deakin University,,Geelong,3216,Australia
3.Faculty of Business Administration,University of Macau,,Macau,China
4.School of Computer Science,Xi'an Shiyou University,,710065,China
Recommended Citation
GB/T 7714
Law,Rob,Li,Gang,Fong,Davis Ka Chio,et al. Tourism demand forecasting: A deep learning approach[J]. Annals of Tourism Research,2019,75:410-423.
APA Law,Rob,Li,Gang,Fong,Davis Ka Chio,&Han,Xin.(2019).Tourism demand forecasting: A deep learning approach.Annals of Tourism Research,75,410-423.
MLA Law,Rob,et al."Tourism demand forecasting: A deep learning approach".Annals of Tourism Research 75(2019):410-423.
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