Application of combined model with DGM(1, 1) and linear regression in grain yield prediction. Issue 1 (5th February 2018)

• Record Type: Journal Article
• Authors: Li, Bingjun; Yang, Weiming; Li, Xiaolu
• Is Part Of: Grey systems. Volume 8:Issue 1(2018)
• Page Start: 25
• Page End: 34
• Publication Date: 2018-02-05
• Subjects: Grey systems modelling and prediction -- Practical applications of grey models -- Combined grey models; Cybernetics -- Periodicals; Systems engineering -- Periodicals; 003.5
• DOI: 10.1108/GS-07-2017-0020
• Languages: English
• ISSNs: 2043-9377

Abstract

Abstract : Purpose: The purpose of this paper is to address and overcome the problem that a single prediction model cannot accurately fit a data sequence with large fluctuations. Design/methodology/approach: Initially, the grey linear regression combination model was put forward. The Discrete Grey Model (DGM)(1, 1) model and the multiple linear regression model were then combined using the entropy weight method. The grain yield from 2010 to 2015 was forecasted using DGM(1, 1), a multiple linear regression model, the combined model and a GM(1, N) model. The predicted values were then compared against the actual values. Findings: The results reveal that the combination model used in this paper offers greater simulation precision. The combination model can be applied to the series with fluctuations and the weights of influencing factors in the model can be objectively evaluated. The simulation accuracy of GM(1, N) model fluctuates greatly in this prediction. Practical implications: The combined model adopted in this paper can be applied to grain forecasting to improve the accuracy of grain prediction. This is important as data on grain yield are typically characterised by large fluctuation and some information is often missed. Originality/value: This paper puts the grey linear regression combination model which combines the DGM(1, 1) model and the multiple linear regression model using the entropy weight method to determine the results weighting of the two models. It is intended that prediction accuracy can be improved through the combination of models used within this paper.