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The purpose of this paper is to further improve accuracy of the grain yield prediction and enhance the robustness of the prediction algorithm. The method mainly involves the knowledge included Gray Theory and Multiple Linear Regression. Readers can refer to Headline 2 to understand. Firstly, this method analyzed the factors that affected the grain yield, smoothed the data of the previous influence factors exponentially, used the gray theory to iteratively predict the new impact factor data, and then automatically selected the factors with high correlation degree through the influence factor correlation analysis. Finally, different treatment processed grain yield data to reduce the fluctuation of the data. To predict future grain yield used multiple regression and residual correction. You can check the title 3 for a detailed solution. The forecasting method proposed in this paper has a good prediction effect, and the relative error of annual average prediction is less than 5%.

Combination Forecast Method of the Output of Grain Based on the Exponential Smoothing and Differential Treatment