摘要
以辣椒价格为研究对象,用天津市10个采集点2013年1月1日—12月30日的辣椒日度市场价格数据作为样本,借助小波神经网络智能分析方法,构建了天津市辣椒价格时间序列预测模型。结果表明,模型预测误差率小于0.01,预测值与实际值之间的相关性R2为0.8438,可准确预测辣椒价格走势。该模型的构建为辣椒市场监测预警提供技术支持,也可为相关政策出台提供参考。
By wavelet neural network intelligent analysis method, choosing pepper prices as study object, pepper retail price data from ten collection sites in Tianjin city from January 1 st, 2013 to December 30th, 2013 as samples, the pepper price time series prediction model to test price model was builded. Results showed that, model prediction error rate was less than 0. 01, and the correlation of predicted value and actual value was 0. 8438, showing that the model could accurately predict pepper price movements. The establishment of the model would provide technical support for pepper market monitoring and early warning and references for related policies.
出处
《中国食物与营养》
2014年第12期40-42,共3页
Food and Nutrition in China
基金
"十二五"国家科技支撑计划重点项目"农业生产与市场流通匹配管理及信息服务关键技术研究与示范"(项目编号:2012BAH20B04)
国家自然基金项目"中国粮食生产消费协调度测定模型构建及实证研究"(项目编号:41201599)
公益性科研院所基本科研业务费专项资金"标准化农情信息采集技术研究与监测系统构建"(项目编号:2014-J-012)
关键词
小波神经网络
辣椒
价格
时间序列
预测模型
wavelet neural network
pepper
price
time series
prediction model
