基于信息粒化和PSO-SVR模型的棉花价格波动区间和变化趋势预测  

Prediction of fluctuation range and change trend of cotton price based on fuzzy information granulation and PSO-SVR model

作　　者：张永礼[1] 赵蕾[1] 董志良[1] 

机构地区：[1]石家庄经济学院,河北石家庄050031

出　　处：《广东农业科学》2015年第11期180-185,共6页Guangdong Agricultural Sciences

基　　金：河北省社会科学发展研究重点项目(2015020203);河北省社会科学发展研究青年项目(2015041229);河北省人力资源和社会保障研究项目(JRSHZ-2015-01032)

摘　　要：农产品价格的准确预测对农民规避市场风险、提高农业收入和国家农业宏观调控具有重大意义。以国家棉花价格A指数的预测为例,提出了一种基于模糊信息粒化和粒子群优化支持向量回归机(PSO-SVR)的农产品价格预测时序回归模型。该模型首先使用模糊信息粒化方法,将原始国家棉花价格A指数时间序列数据映射为包含最小值Low、中值R、最大值Up 3个参数的模糊信息粒,然后使用粒子群优化算法PSO寻找支持向量回归机(SVM)模型的最佳参数c和g,最后,再使用优化后的支持向量回归机(SVM)模型预测国棉价格A指数未来波动区间和变化趋势。实证结果表明,基于模糊信息粒化和PSO-SVR时序回归模型对国棉价格A指数的预测准确有效。Accurately predicting the prices of agricultural products is very important for evading market risk, increasing agricultural income and government macroeconomic regulation. With national cotton prices prediction as an example, this paper proposed a SVM prediction model of agricultural products prices based on fuzzy information granulation and particle swarm optimization algorithm. Firstly, the original national cotton prices A index time series data were transformed into fuzzy information granulation particles made up of Low, R and Up. Secondly, particle swarm optimization algorithm was used to find the best parameters c and g for SVM model. Finally, cotton price fluctuation range and change trend in the future were predicted by the optimized SVM regression model. The empirical analysis showed that the PSO-SVM model was effective for the prediction of cotton price fluctuation range and change trend.

关 键 词：模糊信息粒化 粒子群优化算法 支持向量机 农产品价格 

分 类 号：F326.12[经济管理—产业经济]