Data source combination for tourism demand forecasting

Mingming Hu, Haiyan Song

Research output: Journal article publicationJournal articleAcademic researchpeer-review

8 Citations (Scopus)


Search engine data are of considerable interest to researchers for their utility in predicting human behaviour. Recently, search engine data have also been used to predict tourism demand (TD). Models developed based on such data generate more accurate forecasts of TD than pure time-series models. The aim of this article is to examine whether combining causal variables with search engine data can further improve the forecasting performance of search engine data models. Based on an artificial neural network framework, 168 observations during 2005–2018 for short-haul travel from Hong Kong to Macau are involved in the test, and the empirical results suggest that search engine data models with causal variables outperform models without causal variables and other benchmark models.

Original languageEnglish
JournalTourism Economics
Publication statusPublished - 30 Aug 2019


  • artificial neural network
  • causal economic variables
  • forecast accuracy
  • search engine
  • tourism demand

ASJC Scopus subject areas

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

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