Study and application of grain yield forecasting model

There are some poor accuracy problems of grain yield prediction. GM (1, 1) prediction model and ARIMA (1,1,1) prediction model were established according to Jilin Province 1998-2011 grain yield data. In the same training sample, 2 kinds of methods were used to forecast grain yield. The average error...

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Bibliographic Details
Published in2015 4th International Conference on Computer Science and Network Technology (ICCSNT) Vol. 1; pp. 652 - 656
Main Authors Xu Xingmei, Cao Liying, Zhou Jing, Su Fengyan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2015
Subjects
Analytical models
Data models
Error probability
Forecasting
grey system
Mathematical model
prediction
Predictive models
residual analysis
time series
Time series analysis
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Summary:There are some poor accuracy problems of grain yield prediction. GM (1, 1) prediction model and ARIMA (1,1,1) prediction model were established according to Jilin Province 1998-2011 grain yield data. In the same training sample, 2 kinds of methods were used to forecast grain yield. The average error is 7.88% and 12.32%, the average precision accuracy is 92.12% and 87.68% respectively. The test results show that the average prediction accuracy of grey system is higher than that of the time sequence model, and it can be applied to the prediction of grain yield.
DOI:10.1109/ICCSNT.2015.7490829