Abstract
In the classical multivariate prediction model, most research studies focused on the selection of relevant behaviour factors and the stability of historical data for improving the predicting accuracy of the main behaviour factor, and the historical data of the main behaviour factor have never been considered as one relevant behaviour factor, which in fact can be the first key impact factor; besides, the historical data can directly predict the main behaviour in the time series forecasting model, such as the ARIMA model. In this paper, one modified MLR model combined with time series forecasting theory is presented and applied in grain consumption forecasting. In the proposed model, to improve the current grain consumption forecasting, how to select impact factors is also discussed by combining the grey relational degree and Pearson correlation coefficient with given weights, and the optimal preprocessing parameter by the moving average filtering is computed for eliminating the abnormal points and stabilizing the data. Finally, the selected main impact factors are inputted to the proposed modified MLR model for forecasting grain consumption. Simulation results have shown that the five-year mean absolute percentage error of ration and feed grain is 2.34% and 3.27%, respectively, and the prediction accuracy has improved up to 2 times compared with the BP model and LSTM model. Moreover, the robustness of the model is verified by prediction analysis at different time intervals of historical data.
Highlights
With the change of social, economic, and environmental factors, the grain consumption structure appears to have new features. ere have been various prediction methods by research methods, focuses, and perspectives, which can be divided into qualitative analysis and quantitative analysis
The historical data of prediction variable will be considered as one key impact factor, which will be imputed to the multivariate linear regression (MLR) model with the other chosen impact factors. e proposed modified MLR model can be expressed as yt β0 + β1x1(t) + β2x2(t) + · · · βnxn(t) + φ1yt− 1 + φ2yt− 2 + φ3yt− 3 + · · · + φnyt− n, (22)
The MLR model and modified MLR model are established based on these optimal preprocessing methods
Summary
With the change of social, economic, and environmental factors, the grain consumption structure appears to have new features. ere have been various prediction methods by research methods, focuses, and perspectives, which can be divided into qualitative analysis and quantitative analysis. Gao used the time series method to calculate per capita grain consumption by selecting a national sample of residents and predicted total food consumption. He said that in 2020, China’s total grain consumption will reach to 595 million tons [3]. The impact factors and historical data features are more important for the grain consumption prediction model. It is not enough for the more comprehensive analysis. (2) To minimize the prediction error of grain consumption, we combine the advantages of two models, and one new modified MLR model combined with time series forecasting theory based on data barycenter is proposed
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