Morbidity Analysis on Grey Forecast Model

Chuanfeng Li, Yunxing Shu, Congjun Rao
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Abstract

This paper studies on the morbidity problem for the GM(1,1) model with the accumulating method based on the condition number theory. It proves that we can get a new healthier model by making a multiple transformation on the initial data, and the transformation does not affects the precision of the model. Therefore, the morbidity problem for the GM(1,1) model has been resolved very well with the accumulating method. Furthermore, we give an example to prove that the proposed method can ensure the stability and reliability of the estimation with high quality in the grey system. As a result, it is certain that the GM(1,1) model with the accumulating method has a good application in the future indeed.
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灰色预测模型的发病率分析
基于条件数论的累积方法研究了GM(1,1)模型的病态问题。证明了对初始数据进行多次变换可以得到一个新的更健康的模型,并且变换不影响模型的精度。因此,用累积法很好地解决了GM(1,1)模型的病态问题。最后通过实例验证了该方法在灰色系统中能够保证高质量估计的稳定性和可靠性。由此可以肯定,采用累积法的GM(1,1)模型在未来确实具有良好的应用前景。
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