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Chinese Journal of Management Science ›› 2024, Vol. 32 ›› Issue (9): 48-58.doi: 10.16381/j.cnki.issn1003-207x.2021.0610

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A Matrix Autoregressive Time-delay Grey Multivariable Model for Three-parameter Interval Grey Number Sequences

Yunjie Mei1,Xiangyan Zeng1(),Shuli Yan2   

  1. 1.School of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin 541004, China
    2.School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
  • Received:2021-03-29 Revised:2022-04-15 Online:2024-09-25 Published:2024-10-12
  • Contact: Xiangyan Zeng E-mail:zengxyhbyc@163.com

Abstract:

The existing grey models can only predict three-parameter interval grey number sequences indirectly, and the influence of time-delay effect is seldom considered. In order to improve the prediction accuracy of grey model for three-parameter interval grey numbers, a new model considering time-delay effect is proposed. Firstly, the autoregressive time-delay terms of system characteristic are introduced into the traditional grey models. Further, a matrix autoregressive time-delay grey multivariable model (MARGM(1, N)) is proposed by taking the variables as three-dimensional column vectors and setting the parameters as matrices. The integrity of the three-parameter interval grey numbers is maintained and the relations between the boundaries are connected, so that the three-parameter interval grey number sequences can be modeled and predicted directly by MARGM(1, N). MARGM(1, N) is used to forecast the output of China's secondary industry and tertiary industry. The results show that the models considering the time-delay effect have better fitting effect, the matrix models have higher prediction accuracy, and MARGM(1, N) has the best performance in both fitting and forecasting compared with the competition models. Overall, MARGM(1, N) is feasible and effective. In application, it is helpful for relevant departments to make correct policies to coordinate economic development.

Key words: three-parameter interval grey numbers, grey multivariable model, autoregression, time-delay effect

CLC Number: