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

ABSTRACT

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.

KEYWORDS:

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

Acknowledgments

The authors are grateful to the editors and the anonymous reviewers for their comments and suggestions.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCiD

Binru Zhang http://orcid.org/0000-0003-4495-4060

Additional information

Funding

This work was jointly supported by grants from the National Natural Science Foundation of China [grant numbers 71373023, 41101111 and 71571044], and a funding project for Academic Human Resources Development in Beijing Union University [grant number Rk100201509].