Grain consumption dynamic prediction method

Abstract

The invention provides a grain consumption dynamic prediction method comprising the following steps in turn: (1) grain consumption and the values of influence factors of multiple years before the nth year are acquired, degree of association is calculated and the process enters step (2); (2) the values of the influence factors of the (n+1)th year are calculated, and the process enters step (3); (3) the grain consumption of the (n+1)th year is calculated; the grain consumption of the (n+m)th year requires to be predicted and the process enters step (4); and the grain consumption of the (n+m)th year does not require to be predicted and the process ends, wherein n is the year of which the grain consumption and the values of the influence factors can be inquired, and n+m is the year of which the grain consumption and the values of the influence factors cannot be inquired; and (4) the process returns to the step (1), and n is enabled to be equal to n+1 until the grain consumption of the (n+m)th year is obtained. The grain consumption of our country is predicted by utilizing the dynamic method through combination of multivariate regression analysis, and the regression parameters of a multivariate regression equation continuously change so that accuracy of prediction can be enhanced and the method can be applied to long-term prediction.

Description

technical field [0001] The invention belongs to the field of grain forecasting, and in particular relates to a dynamic forecasting method for grain consumption. Background technique [0002] Grain is the foundation of the country. Our country guarantees the supply of grain by establishing granaries. Although it is relatively simple to realize the supply of grain simply through the granary, it cannot reasonably increase or decrease the grain in the granary according to the grain consumption situation. Implementation costs manpower and money. [0003] In order to solve the above problems, the prior art provides a method of predicting grain consumption through multiple regression equations. According to this method, relevant personnel can know the future grain consumption, so as to reasonably arrange the grain volume in the granary. However, when the existing grain consumption forecasting method is forecasting, only a regression parameter and a random error are obtained accord...

AI Technical Summary

Problems solved by technology

However, when the existing grain consumption forecasting method is forecasting, only a regression parameter and a random error are obtained according to the correlation between the known influencing factors and the grain consumption, and the grain consumption is obtained by combining the regression parameter and the random error. Consumption, the prediction accuracy is low, and the reference value is not great

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