关键词: 灰色GM(1, 1)模型, 函数变换, 变形预测, 数据序列, 光滑度

Abstract: To solve the problems that the original data sequence does not satisfy function ln(x) transformation requirements and the data sequence after transformation does not satisfy the modeling requirements, in this paper, the method of function ln(x+c) (c>0) transformation against modeling data was put forward. Theoretically it has been proved that the smooth degree of data series for modeling after this transformation could be enhanced, and the GM(1,1) model precision based on the data transformation was also improved. A method based on modeling data sequence for approximatively?determining the best constant c was given. Additionally, this article selected deformation data sequences that were monotonically increasing and met modeling requirements. The results are obtained that the measured value is close to the predicted value. The practical application showed the effectiveness and practicability of the proposed approach. This method makes up the deficiency of the original data sequence and logarithmic transformation sequence, and broadens the application range of the grey GM (1,1) model, so that the fitting accuracy and the prediction accuracy of the model can be improved.

Key words: grey GM（1,1）model；function transformation, deformation prediction, data sequence, smooth degree