Systems Engineering and Electronics ›› 2018, Vol. 40 ›› Issue (3): 595-602.doi: 10.3969/j.issn.1001-506X.2018.03.17

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Multivariable grey forecasting model based on interaction effect and its application

DING Song1,2, DANG Yaoguo1, XU Ning3, WANG Junjie1   

  1. 1. College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China; 2. Department of System Design Engineering, University of Waterloo, Waterloo N2L 3G1, Canada; 3. College of Management Science and Engineering, Nanjing Audit University, Nanjing 211815, China
  • Online:2018-02-26 Published:2018-02-24

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Abstract:

To address the drawbacks of traditional GM(1, N ), which do not consider the interaction effects among driving factors, a model, abbreviated as IEGM(1, N ), is proposed by introducing the interactive terms into the GM(1, N ) model after studying the concept of interaction effects and problems of the classic multivariable model. Subsequently, the methods of estimating the parameters are put forward. Then, two derived models are built to extend the application areas of the novel model, and their modeling conditions and application scopes are studied. Finally, an actual example about the output values of high-tech industry in Jiangsu province is studied to demonstrate the efficacy of IEGM(1, N ) and its derived model. Empirical results show that the novel model considering interaction effects can achieve a better simulative and predictive precision than the other two models to solve the forecasting problems of a system, which are affected by many factors having interaction effects.

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