Abstract. 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%.
In order to predict the grain yield of the country accurately, considering the periodical fluctuation of the data, the method of time series is used. Firstly, the stability and the relativity of the yield series from year 1980 to 2009 are analyzed, and the first-order difference of which is calculated to get a stationary series. Then, after comparing the value of AIC of different models, the forecasting model ARIMA(5,1,5) is selected as the best one, and the performance of which is tested. Lastly, the grain yields from year 2010 to 2012 are predicted by three different methods, the results shown that, the prediction error of the model ARIMA(5,1,5) is 4.478%, the error of the grey model GM(1,1) is 6.78%, and the error of the second exponential smoothing method is 7.682%, thus, the model ARIMA(5,1,5) is more suitable to forecast the grain yield in short-term.
Abstract. The paper uses the machine leaning algorithm to analyses grain supply and demand of China. For the sake of small samples, support vector regression is used to forecast the tread of grain supply and demand. From the result, it can be found that support vector regression can get good performance using some different metrics. The result also shows that both grain supply and demand will increase in long tread. At last, some suggestions about grain supply and demand are given.
Abstract. In recent years, the demand and supply of grain has dramatically changed. The paper uses the intelligent information processing method to analyse grain supply and demand of China. The gray model and Back Propagation neural network are used to forecast the trend of grain supply and demand in the thirteen-five period. The result shows that both grain supply and demand will increase in long trend, but grain demand has more than grain supply. The forecasting grain supply growth ratio will be separately 0.88% and 0.79%, the grain demand will be increased 32.2% and 19.5% in 2020 using the grey model and BP, compared with the supply and demand in 2014. At last, the suggestions are given. In order to guarantee the grain safety, scientific method should be used to monitor and analysis the grain market.
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