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Optimizing the Grey GM(1,N) Model by Rebuilding All the Back Ground Values

Abstract The GM(1,N) model is a very important prediction model of the grey system. But the inherent defect of GM(1,N), which may cause very large error, is still there. This paper analyzes the source of the error of GM(1,N) and reveals that it’s all the back ground values that effect the precision and applicability of GM(1,N). Three methods are employed to revise the GM(1,N) model. The simulation test shows the new models perform with higher precision and robustness. Even in some extreme cases, in which the original GM(1,N) is invalid, the new models are still valid and perform well.