Tourism demand forecasting: a deep learning approach
Version 2 2024-06-04, 01:55Version 2 2024-06-04, 01:55
Version 1 2019-02-20, 16:31Version 1 2019-02-20, 16:31
journal contribution
posted on 2024-06-04, 01:55 authored by R Law, Gang LiGang Li, DKC Fong, X Han© 2019 Elsevier Ltd 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
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Journal
Annals of tourism researchVolume
75Pagination
410-423Location
Amsterdam, The NetherlandsISSN
0160-7383Language
engPublication classification
C1 Refereed article in a scholarly journalPublisher
ElsevierUsage metrics
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