基于灰色——马尔可夫模型的农产品产量预测方法

doi:10.11896/JsJkx.190700126

计算机科学 ›› 2020, Vol. 47 ›› Issue (6A): 535-539.doi: 10.11896/JsJkx.190700126

• 数据库 & 大数据 & 数据科学 • 上一篇下一篇

基于灰色——马尔可夫模型的农产品产量预测方法

马创¹, 袁野², 尤海生²

1 重庆邮电大学软件学院重庆 400065;
2 重庆邮电大学计算机科学与技术学院重庆 400065

发布日期:2020-07-07
通讯作者: 马创(machuang@cqupt.edu.cn)
基金资助:
国家自然科学基金面上项目(6172099);重庆市创新创业示范团队培育计划(CSTC2017kJrc-cxcytd0063);重庆市技术创新与应用示范重大主题专项项目(CSTC2018JSZX-CYZTZX0178,CSTC2018JSZX-CYZTZX0185);重庆市基础科学与前沿技术研究项目(CSTC2017JcyJAX0270,CSTC2018JcyJA0672,CSTC2017JcyJAX0071)

Agricultural Product Output Forecasting Method Based on Grey-Markov Model

MA Chuang¹, YUAN Ye² and YOU Hai-sheng²

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).

摘要/Abstract

摘要： 粮食在农产品中扮演着举足轻重的地位,粮食产量一定程度决定了国家粮食供给能力及温饱安全水平,因此对粮食产量进行精准预测的研究具有重要的价值。鉴于粮食产量受多种复杂因素的影响具有极强的波动性和随机性,为提高粮食产量预测的准确性,针对我国粮食产量的特点,文中提出一种基于灰色模型与马尔可夫模型相融合的模型,用马尔可夫模型对灰色模型的预测值进行修正以达到对粮食产量进行周期性预测。通过选取我国2009年至2018年的粮食年产量数据(数据来源:国家数据统计局)进行分析研究。所提方法首先利用灰色模型对产量进行预测,计算预测误差,通过对误差序列利用灰色建模修正产量预测数据;其次,根据粮食年产量预测精度,将粮食年产量数据划分成若干状态,进而可求出各阶状态转移概率和状态转移概率矩阵;最后,通过建立新陈代谢后的灰色模型对粮食年产量进行预测得到预测结果,利用马尔可夫模型对预测结果进行残差值进行修正以达到提高粮食产量预测值精度。仿真实验分别将单一灰色模型和灰色马尔可夫模型的预测精度进行比较。结果表明,灰色模型预测值在2009－2013 年的年产量预测中误差小于 1.00%,但随着年份的增加,由于粮食年产量间的相互影响导致预测精度变差,2014－2018年的年产量预测误差均高于1.00%;灰色-马尔可夫模型年产量预测误差均小于0.30%,平均误差为 0.12%,相较于传统灰色模型及马尔可夫模型,其预测的准确率大幅度提高。

关键词: 灰色模型, 粮食产量, 马尔可夫模型

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

中图分类号:

TP181

马创, 袁野, 尤海生. 基于灰色——马尔可夫模型的农产品产量预测方法[J]. 计算机科学, 2020, 47(6A): 535-539. https://doi.org/10.11896/JsJkx.190700126

MA Chuang, YUAN Ye and YOU Hai-sheng. Agricultural Product Output Forecasting Method Based on Grey-Markov Model[J]. Computer Science, 2020, 47(6A): 535-539. https://doi.org/10.11896/JsJkx.190700126

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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.

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基于灰色——马尔可夫模型的农产品产量预测方法

Agricultural Product Output Forecasting Method Based on Grey-Markov Model

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