基于SCGM(1,1)c-Markov模型的茶叶产量预测研究  

Study on Tea Output Forecasting Based on SCGM(1,1)c-Markov Model

作　　者：胡林燕 徐士元 HU Lin-yan;XU Shi-yuan(School of Economics and Management,Zhejiang Ocean University,Zhoushan,Zhejiang 316000)

机构地区：[1]浙江海洋大学经济与管理学院,浙江舟山316000

出　　处：《安徽农业科学》2022年第22期229-233,共5页Journal of Anhui Agricultural Sciences

摘　　要：为了提高茶叶产量的预测精度,以单因子系统云灰色预测模型[System cloud grey model,SCGM(1,1)c]为基础,提出基于马尔可夫预测理论修正的SCGM(1,1)c-Markov预测模型。首先,阐述SCGM(1,1)c预测模型的建模过程;其次,结合SCGM(1,1)c与马尔科夫预测理论的优点构造SCGM(1,1)c-Markov预测模型;最后,以2006—2020年浙江省茶叶产量的实际数据为样本,使用以上2种模型分别进行预测计算,并作相应预测值的拟合曲线图。结果表明:经过马尔可夫预测理论修正的SCGM(1,1)c-Markov模型的预测精度和拟合性较SCGM(1,1)c预测模型精度有大幅度提高,为茶叶产量的预测研究提供了一种新的方法。In order to improve the prediction accuracy of tea output,a SCGM(1,1)c-Markov prediction model was proposed based on the SCGM(1,1)c model of single factor cloud grey model.The research methods are as follows:Firstly,the modeling process of SCGM(1,1)c prediction model is described.Secondly,the SCGM(1,1)c-Markov prediction model is constructed by combining the advantages of SCGM(1,1)c and Markov prediction theory.Finally,taking the tea output in Zhejiang Province from 2006 to 2020 as samples,the above two models were used for prediction respectively,and the fitting curves of the corresponding predicted values were made.The results show that the accuracy and fitting of SCGM(1,1)c-Markov model modified by Markov prediction theory are much higher than that of SCGM(1,1)c model,which provides a new method for the research of tea output prediction.

关 键 词：茶叶产量 云灰色预测 马尔科夫理论 浙江省 

分 类 号：S-9[农业科学]