A novel hybrid model for tourist volume forecasting incorporating search engine data

Binru Zhang, Xiankai Huang, Nao Li, Chun Hung Roberts Law

Research output: Journal article publicationJournal articleAcademic researchpeer-review

47 Citations (Scopus)


The precise prediction of tourism demand has long presented a challenge for both tourism professionals and academics. Tourist volume forecasting is a nonlinear problem, support vector regression (SVR) can approximate a nonlinear system with enough precision, but parameters tuning has always been an obstacle to developing SVR with good generalization potential. Furthermore, previous research mainly used historical observations of tourism demand as the inputs of SVR. This study introduces an approach that hybridizes SVR with the Bat algorithm (BA), namely BA-SVR, to forecast tourist volume by incorporating search engine data. In this model, BA is used to adjust the SVR parameters. To validate our proposed approach, tourist volume data for China’s Hainan province from August 2008 to October 2015 were used in conjunction with corresponding search engine data as numerical examples. The 12-month simulation forecasts indicate that the BA-SVR is an effective method that can outperform its traditional counterparts.
Original languageEnglish
Pages (from-to)245-254
Number of pages10
JournalAsia Pacific Journal of Tourism Research
Issue number3
Publication statusPublished - 4 Mar 2017


  • Bat algorithm
  • Hybrid model
  • search engine data
  • support vector regression
  • tourist volume forecasting

ASJC Scopus subject areas

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


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