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Group pooling for deep tourism demand forecasting
journal contribution
posted on 2020-05-01, 00:00 authored by Y Zhang, Gang LiGang Li, Birgit Muskat, R Law, Y YangAdvances in tourism demand forecasting immensely benefit tourism and other sectors, such as economic and resource management studies. However, even for novel AI-based methodologies, the challenge of limited available data causing model overfitting and high complexity in forecasting models remains a major problem. This study proposes a novel group-pooling-based deep-learning model (GP–DLM) to address these problems and improve model accuracy. Specifically, with our group-pooling method, we advance the tourism forecasting literature with the following findings. First, GP–DLM provides superior accuracy in comparison with benchmark models. Second, we define the novel dynamic time warping (DTW) clustering quantitative approach. Third, we reveal cross-region factors that influence travel demands of the studied regions, including “travel blog,” “best food,” and “Air Asia.”
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Journal
Annals of Tourism ResearchVolume
82Article number
102899Pagination
1 - 17Publisher
ElsevierLocation
Amsterdam, The NetherlandsPublisher DOI
ISSN
0160-7383Language
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2020, Elsevier LtdUsage metrics
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