连锁超市零售商品销量的多步预测研究

Multi-Step Forecasting Research on Retail Commodity’s Sales Volume of Supermarket

文章通过分析连锁超市部分商品的销售特征,采取传统时间序列法、神经网络模型(ANN)两类基础方法对较有代表性的销售数据序列进行预测。文章将对原有的直接多步(Direct Multi-Step Ahead,DMSA)预测思路进行改进,采用改进的直接多步预测法对销量进行三步超前预测和五步超前预测,并将结果与采用DMSA、间接多步预测(Recursive Multi-Step Ahead,RMSA)和可变时间尺度法(Variable Time Scale,VTS)的实验结果进行对比,发现改进的直接多步预测法的预测精度有了一定的提升。

By analyzing the sales characteristics of some commodities in supermarket,this paper adopts traditional time series method and neural network(ANN)model to predict the typical sales data sequence.In this paper,the original direct multi-step ahead(DMSA)prediction method is improved.The improved direct multi-step ahead prediction method is used to predict the sales with three steps and five steps ahead.By comparing with the experimental results of direct multi-step ahead prediction method,recursive multi-step ahead prediction method and variable time scale method,it finds that the improved direct multi-step ahead prediction method has better prediction accuracy.