Title: Prediction of Grain Production In Henan Province by Grey Combined Model
Abstract: One-variable linear regression analysis could reflect the tendency of straight line while GM(1,1)could do it better when the sequence tends to change as exponential function.However,if a raw sequence has straight tendency in a whole but there exists big error between actual and fitted values among some points,line function will no more predict accurately the changing tendency of the data sequence.To solve this question,firstly,the raw data sequence with some abnormal data is classified into two parts:aberrant data and normal data,more over,we could classified the aberrant points into upper and lower aberrant points;secondly,borrowing the principle of grey disaster,we can make use of GM(1,1)to forecast the possible aberrant date points in the future based on the aberrant data,and for other normal data points left,one new linear regression function can be applied to get a forecasted value.By appling the combined method to the prediction of the grain production of He'nan province,we find the new method could achieve better forecasting results compared with other forecasting models,and make up for some deficiencies in GM(1,1)model and linear regression in a sense.
Publication Year: 2008
Publication Date: 2008-01-01
Language: en
Type: article
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Cited By Count: 1
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