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基于Lasso和支持向量机的粮食价格预测
引用本文:喻胜华,龚尚花.基于Lasso和支持向量机的粮食价格预测[J].湖南大学学报(社会科学版),2016(1):71-75.
作者姓名:喻胜华  龚尚花
作者单位:(湖南大学 经济与贸易学院,湖南,长沙410079)
摘    要:首先利用Lasso方法在影响粮食价格波动的众多因素中选出了粮食储备、粮食生产成本、粮食产量、粮食政策、生产需求、贸易需求、心理预期等7个主要影响因素;然后在Lasso变量选择的基础上利用支持向量机进行粮食价格的回归与预测,同时,把Lasso、支持向量机、Lasso-支持向量机及ARIMA方法的拟合预测效果进行比较。实证结果表明:Lasso-支持向量机组合预测方法的拟合预测效果要优于另外三种预测方法。

关 键 词:粮食价格预测  影响因素  Lasso  支持向量机

A Study on Grain Price Prediction Based on Lasso and Support Vector Machine
YU Sheng-hu,GONG Shang-hua.A Study on Grain Price Prediction Based on Lasso and Support Vector Machine[J].Journal of Hunan University(Social Sciences),2016(1):71-75.
Authors:YU Sheng-hu  GONG Shang-hua
Institution:(School of Economics and Trade,Hunan University,Changsha410079,China)
Abstract:The paper firstly chooses grain reserves, grain production cost, grain production, grain policy, production demand, trade demand, and psychological expectations as seven main factors influencing grain price volatility based on the Lasso method. And then, according to Lasso variable selection, we conduct the regression and forecasting of grain prices with the help of support vector machine (SVM). At the same time, by comparing the fitting prediction effect of the Lasso, support vector machine (SVM), Lasso-SVM and ARIMA method, the empirical results show that the Lasso-SVM of fitting forecasting effect considerably overmatches the other three methods.
Keywords:grain price prediction  influencing factors  Lasso  support vector machine
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