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Using a Grey–Markov model optimized by Cuckoo search algorithm to forecast the annual foreign tourist arrivals to China

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  • Sun, Xu
  • Sun, Wangshu
  • Wang, Jianzhou
  • Zhang, Yixin
  • Gao, Yining

Abstract

With the rapid development of the international tourism industry, it has been a challenge to forecast the variability in the international tourism market since the 2008 global financial crisis. In this paper, a novel CMCSGM(1, 1) forecasting model is proposed to address how forecasting precision is affected by the volatility of the tourism market. The Markov-chain grey model is adopted for its emphasis on the small-sample observations and exponential distribution samples. Additionally, the optimal input subset method and the Cuckoo search optimization algorithm are applied to improve the performance of the Markov-chain grey model. The experimental study of the forecasting of the annual foreign tourist arrivals to China indicates that the proposed CMCSGM(1, 1) model is considerably more efficient and accurate than the conventional MCGM(1, 1) models.

Suggested Citation

  • Sun, Xu & Sun, Wangshu & Wang, Jianzhou & Zhang, Yixin & Gao, Yining, 2016. "Using a Grey–Markov model optimized by Cuckoo search algorithm to forecast the annual foreign tourist arrivals to China," Tourism Management, Elsevier, vol. 52(C), pages 369-379.
    • Handle: RePEc:eee:touman:v:52:y:2016:i:c:p:369-379
    DOI: 10.1016/j.tourman.2015.07.005
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    References listed on IDEAS

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    Cited by:

    1. Li, Xin & Pan, Bing & Law, Rob & Huang, Xiankai, 2017. "Forecasting tourism demand with composite search index," Tourism Management, Elsevier, vol. 59(C), pages 57-66.
    2. Wei, Sun & Yanfeng, Xu, 2017. "Research on China's energy supply and demand using an improved Grey-Markov chain model based on wavelet transform," Energy, Elsevier, vol. 118(C), pages 969-984.
    3. Liu, Xiaomei & Xie, Naiming, 2019. "A nonlinear grey forecasting model with double shape parameters and its application," Applied Mathematics and Computation, Elsevier, vol. 360(C), pages 203-212.
    4. Song, Haiyan & Qiu, Richard T.R. & Park, Jinah, 2019. "A review of research on tourism demand forecasting," Annals of Tourism Research, Elsevier, vol. 75(C), pages 338-362.
    5. Hu, Yi-Chung, 2023. "Air passenger flow forecasting using nonadditive forecast combination with grey prediction," Journal of Air Transport Management, Elsevier, vol. 112(C).

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