摘要
电子商务交易额是衡量一个国家或者地区电子商务发展状况的重要指标,精确的交易额预测在国家的电子商务战略决策中具有重要的意义。针对历年中国电子商务交易额的数据特征,提出了基于灰色理论与RBF神经网络算法的组合预测方法,并对未来的电子商务交易额进行预测,将预测结果与单一的GM (1,1)模型和RBF神经网络模型进行比较,显示出较高的精度,为电子商务交易额预测提供了新的方法。
E-commerce trade volume is an important index to measure a country or a region＇s E-commerce development situation,and an accurate volume forecast plays a critical role in making a country＇s E-business strategy. According to the data characteristics of China＇s E-commerce transactions throughout the years, this paper proposed a combined forecast model based on grey theory and RBF neural network algorithm. After predicting the trade volume of China＇s E-commerce, this report compared the combined forecast model with single GM （1,1 ） model and RBF neural network model, which shows the combined forecast model＇s predicted results are much more accurate. Therefore, the combined forecast model can be used as a new method for predicting E-commerce trade volume.
出处
《物流科技》
2016年第6期58-61,共4页
Logistics Sci-Tech
基金
辽宁省教育厅2015年科技计划项目
项目编号:W2015242
