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

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.