The traditional predictive method cannot fully reflect the complex nonlinear characteristics and regularities of automobile and\nparts sales data, so the prediction precision is not high. Thepurpose of this paper is to propose the gray GM(1,1) nonlinear periodic\npredictive model by introducing the seasonal variation index to improve predictive accuracy of the single GM(1,1) model. Firstly,\nthe paper analyzes concept of GM(1,1) and then proposes the gray GM(1,1) nonlinear periodic predictive model to forecast\nautomobile parts sales. Themodel algorithm used gray theory and accumulated technology to generate new data and set up unified\ndifferential equations to find the fitting curve of automobile parts sales prediction by the seasonal variation index to remove\nrandom elements. Lastly, the gray GM(1,1) nonlinear periodic predictive model is used for empirical analysis; the result of\nexample shows that the model proposed in the paper is feasible. The superiority of the proposed predictive model compared with\nthe single gray GM(1,1) model is demonstrated. The reliability of this model is experienced by the accuracy test, which provides a\ntheoretical guidance for the prediction of automobile part sales. And the average relative error is reduced by 8.52% compared with\nthe single GM(1,1) model.
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