Tourism forecasting: A review of methodological developments over the last decade

Eden Xiaoying Jiao, Jason Li Chen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

96 Citations (Scopus)


This study reviewed 72 studies in tourism demand forecasting during the period from 2008 to 2017. Forecasting models are reviewed in three categories: econometric, time series and artificial intelligence (AI) models. Econometric and time series models that have already been widely used before 2007 remained their popularity and were more often used as benchmark models for forecasting performance evaluation and comparison with respect to new models. AI models are rapidly developed in the past decade and hybrid AI models are becoming a new trend. And some new trends with regard to the three categories of models have been identified, including mixed frequency, spatial regression and combination and hybrid models. Different combination components and combination techniques have been discussed. Results in different studies proved superiority of combination forecasts over average single forecasts performance.

Original languageEnglish
Pages (from-to)469-492
Number of pages24
JournalTourism Economics
Issue number3
Publication statusPublished - 1 May 2019
Externally publishedYes


  • forecasting
  • new trends
  • review
  • tourism demand

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Tourism, Leisure and Hospitality Management


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