 Group pooling for deep tourism demand forecastingYishuo Zhang, Gang Li, Birgit Muskat, Rob Law and Yating Yang Annals of Tourism Research, 2020, vol. 82, issue C Abstract: Advances 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.” Keywords: Tourism demand forecasting; AI-based methodology; Group-pooling method; Deep-learning model; Tourism demand similarity; Asia Pacific travel patterns (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:anture:v:82:y:2020:i:c:s0160738320300438 DOI: 10.1016/j.annals.2020.102899 Access Statistics for this article Annals of Tourism Research is currently edited by John Tribe More articles in Annals of Tourism Research from Elsevier |
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