基于时间序列SVR模型的玉米价格预测研究

Corn Price Prediction Based on Time Series SVR Model

研究旨在运用时间序列和支持向量回归(support vector regression,SVR)理论,建立短期玉米价格预测模型,为玉米市场监测预警提供技术支持与决策依据。本研究根据玉米价格序列波动的非线性特征,以河南省2010年1月1日—2019年6月30日的月度数据为研究对象,结合混沌性时间序列与SVR理论,研究一种短期玉米价格预测模型。通过相空间重构的方法对价格序列进行处理,利用重构后的数据训练模型,运用网格交叉验证(GridSearchCV)对研究模型进行优化。得到一种基于时间序列支持向量回归的玉米价格预测模型。将研究模型的预测结果进行对比分析,结果显示,研究模型的预测值更贴近真实值,其平均相对误差(MRE)和均方根误差(RMSE)分别为0.006和20.57,优于传统支持向量回归模型的预测结果。研究模型可以为玉米价格预测提供方法依据,且相较于传统预测方法在精度方面具有优势。

The study aims to establish a short-term corn price forecasting model by using time series and support vector regression(SVR) theory, to provide technical support and decision-making basis for corn monitoring and early warning. Based on the non-linear characteristics of corn price series fluctuations, this study took the monthly data of Henan Province from January 1, 2010 to June 30, 2019 as the research object,and combined a chaotic time series with SVR theory, to study a kind of short-term corn price prediction model.The price sequence was processed by the phase space reconstruction method, the model was trained with the reconstructed data, and the grid cross validation(GridSearchCV) was used to optimize the research model. A corn price prediction model was obtained based on time series support vector regression. By analyzing the prediction results of the research model, it is showed that the prediction value of the research model is closer to the true value, and its average relative error(MRE) and root mean square error(RMSE) is 0.006 and 20.57,respectively, which are superior to the prediction results of the traditional support vector regression model, with more accuracy.