Forecasting tourism demand using search query data: A hybrid modelling approach

Long Wen, Chang Liu, Haiyan Song

Research output: Journal PublicationArticlepeer-review

45 Citations (Scopus)


Search query data have recently been used to forecast tourism demand. Linear models, particularly autoregressive integrated moving average with exogenous variable models, are often used to assess the predictive power of search query data. However, they are limited by their inability to model non-linearity due to their pre-assumed linear forms. Artificial neural network models could be used to model non-linearity, but mixed results indicate that their application is not appropriate in all situations. Therefore, this study proposes a new hybrid model that combines the linear and non-linear features of component models. The model outperforms other models when forecasting tourist arrivals in Hong Kong from mainland China, thus demonstrating the advantage of adopting hybrid models in forecasting tourism demand with search query data.

Original languageEnglish
Pages (from-to)309-329
Number of pages21
JournalTourism Economics
Issue number3
Publication statusPublished - 1 May 2019


  • artificial neural network
  • hybrid specification
  • non-linear model
  • search query data
  • tourism forecasting

ASJC Scopus subject areas

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


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