Using a Grey–Markov model optimized by Cuckoo search algorithm to forecast the annual foreign tourist arrivals to China
Tourism ManagementPublished 28 July 2015
Xu Sun, Wangshu Sun, Jianzhou Wang, Yixin Zhang, Yining Gao
Citations116
SJR quartileQ1
SJR score4.15
SNIP3.74
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TL;DR
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), which is based on the Markov-chain grey model.
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. (C) 2015 Elsevier Ltd. All rights reserved.
Keywords
Decision Sciences
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