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Application of GM(1,1)-Markov chain combined model to China's automobile industry

Author

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  • Guo-Dong Li
  • Daisuke Yamaguchi
  • Masatake Nagai

Abstract

In this paper, we propose a new dynamic analysis model which combines the first-order one-variable grey differential equation model (abbreviated as GM(1,1) model) from grey system theory and the Markov chain model from stochastic process theory. We abbreviate the combined GM(1,1)-Markov chain model to MGM(1,1) model. This combined model takes advantage of the high predicting power of the GM(1,1) model and at the same time takes advantage of the predicting power of Markov chain modelling on the discretised states based on the GM(1,1) modelling residual sequence. For prediction accuracy improvements, Taylor approximation is further applied to the MGM(1,1) model. We call the improved version the T-MGM(1,1) model. As an illustrative example, we use statistical data recording the number of China's privately owned vehicles from 1986 to 2004 for a validation of the effectiveness of the T-MGM(1,1) model.

Suggested Citation

  • Guo-Dong Li & Daisuke Yamaguchi & Masatake Nagai, 2007. "Application of GM(1,1)-Markov chain combined model to China's automobile industry," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 2(3), pages 327-347.
    • Handle: RePEc:ids:ijisen:v:2:y:2007:i:3:p:327-347
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    Cited by:

    1. 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.

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