Group pooling for deep tourism demand forecasting

Yishuo Zhang, Gang Li, Birgit Muskat, Rob Law, Yating Yang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

35 Citations (Scopus)


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.”

Original languageEnglish
Article number102899
JournalAnnals of Tourism Research
Publication statusPublished - May 2020


  • AI-based methodology
  • Asia Pacific travel patterns
  • Deep-learning model
  • Group-pooling method
  • Tourism demand forecasting
  • Tourism demand similarity

ASJC Scopus subject areas

  • Development
  • Tourism, Leisure and Hospitality Management


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