Interpolation grouping sliding gravity center-based nationwide grain consumption prediction method with multiple regression model

Abstract

The invention discloses an interpolation grouping sliding gravity center-based nationwide grain consumption prediction method with a multiple regression model. The method comprises the following stepsin sequence: (1) acquiring original data; (2) acquiring significant influence factors; (3) respectively pretreating grain consumption and two significant influence factors so as to obtain pretreatment data; (4) calculating a sliding gravity center of the pretreatment data; (5) inputting the data obtained in the step (4) into a multiple regression model so as to obtain a primary prediction resultof grain consumption; and (6) performing sliding data gravity center inverse operation on the prediction result obtained in the step (5), thereby obtaining an expected actual prediction value y'1(t) of grain consumption. By adopting the method, prediction errors can be smoothed, data gravity center decrement in the grouping sliding gravity center calculation process can be compensated by additional interpolation pretreatment, meanwhile due to addition of training data of prediction models, the prediction precision of the multiple regression model can be remarkably improved.

Description

technical field [0001] The invention belongs to the technical field of ration consumption forecasting, and in particular relates to a method for forecasting national grain consumption based on a multivariate regression model of interpolation grouping sliding center of gravity. Background technique [0002] Grain consumption is an important part of the national grain consumption. Data analysis shows that with the acceleration of urbanization level construction, the grain consumption presents a decreasing trend year by year, and there are obvious differences in urban and rural ration consumption trends. At present, many scholars have proposed methods such as EMM model method, time series extrapolation method, panel data estimation, statistical analysis and econometric analysis to predict grain consumption, and review the existing methods. There are historical data anomalies in grain consumption prediction methods It is very destructive to the prediction results, the lack of or...

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Problems solved by technology

At present, many scholars have proposed methods such as EMM model method, time series extrapolation method, panel data estimation, statistical analysis and econometric analysis to predict grain consumption, and review the existing methods. There are historical data anomalies in grain consumption prediction methods It is very destructive to the prediction results, the lack of original data leads to large training errors of the prediction model, and the prediction of a single dependent variable.

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