Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 535-539.doi: 10.11896/JsJkx.190700126

• Database & Big Data & Data Science • Previous Articles     Next Articles

Agricultural Product Output Forecasting Method Based on Grey-Markov Model

MA Chuang1, YUAN Ye2 and YOU Hai-sheng2   

  1. 1 School of Software Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China
    2 School of Software Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China
  • Published:2020-07-07
  • About author:MA Chuang, born in 1984, Ph.D, associate professor, is a member of China Computer Federation.His main research interests include complex network, and machine learning.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (6172099),Chongqing Municipal Innovation and Entrepreneurship Demonstration Team Cultivation Program (CSTC 2017kJrc-cxcytd0063);Chongqing Technical Innovation and Application Demonstration MaJor Theme ProJect (CSTC 2018JSZX-CYZTZX0178,CSTC 2018JSZX-CYZTZX0185);Chongqing Basic Science and Frontier Technology Research ProJect (CSTC2017JcyJAX0270,CSTC2018JcyA0672,CSTC2017JcyAX0071).

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Abstract: Grain plays an important role in agricultural products.Grain output determines the country’s grain supply capacity and the level of food and clothing security to a certain extent.Therefore,it is of great value to study the accurate prediction of grain output.In view of the fact that grain output is highly volatile and random due to various complex factors,in order to improve the accuracy of grain output prediction,a model based on the fusion of gray model and Markovmodel is proposed for the characteristics of grain output in China,Markov model is used to modify the forecast value of the gray model to achieve periodic forecast of grain output.Through the selection of my country’s annual grain output data from 2009 to 2018 (data source: National Bureau of Statistics) for analysis and research.The method first uses the gray model to predict output,calculate the forecast error,and uses gray modeling to correct the forecasted output data by using the gray model for the error sequence; second,the annual grain output data is divided into several states through the accuracy of the annual grain output forecast,and then the Find the state transition probabilities and state transition probability matrices of each order; finally,predict the annual grain output by establishing a gray model after the metabolism of Xincheng to obtain the prediction results,and use the Markov model to modify the residual values of the prediction results to achieve improved grain Accuracy of yield forecast.Through simulation experiments,the prediction accuracy of the single gray model and the gray Markov model are compared.The forecast value of the gray model is less than 1.00% in the forecast of annual output from 2009 to 2013.However,as the year increases,the forecast accuracy is deteriorated due to the interaction between the annual grain output.Both are higher than 1.00%.The gray-Markov model’s annual output prediction error is less than 0.30%,and the average error is 0.12%.Compared with the traditional gray model and Markov mo-del,the accuracy of prediction is greatly improved.

Key words: Grain production, Grey model, Markov model

CLC Number: 

  • TP181
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