Tourism demand forecasting: A deep learning approach

Rob Law, Gang Li, Davis Ka Chio Fong, Xin Han

Research output: Journal article publicationJournal articleAcademic researchpeer-review

173 Citations (Scopus)


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

Original languageEnglish
Pages (from-to)410-423
Number of pages14
JournalAnnals of Tourism Research
Publication statusPublished - Mar 2019


  • Attention mechanism
  • Deep learning
  • Feature engineering
  • Lag order
  • Long-short-term-memory
  • Tourism demand forecasting

ASJC Scopus subject areas

  • Development
  • Tourism, Leisure and Hospitality Management


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