基于LSTM-RELM组合模型的电商GMV预测研究  

Research on GMV Prediction of E-commerce Based on LSTM-RELM Combination Model

作　　者：王逸文 王维莉 WANG Yiwen;WANG Weili(Logistics Research Center,Shanghai Maritime University,Shanghai 201306,China)

机构地区：[1]上海海事大学物流研究中心,上海201306

出　　处：《计算机工程与应用》2023年第10期321-327,共7页Computer Engineering and Applications

基　　金：上海市科技创新行动计划项目(19DZ1209600)。

摘　　要：随着互联网的发展,内容营销逐渐成为电商营销的主流,而该类营销的日商品交易总额(gross merchandise volume,GMV)直接关系到企业的库存优化控制与广告投放策略。为了提高预测精度,基于真实电商订单数据集,根据内容营销的指标,分析用户行为对于GMV的影响,提出了一种长短期记忆网络(long short-term memory network,LSTM)与正则化极限学习机(regularized extreme learning machine,RELM)的组合模型LSTM-RELM。实验结果表明,相比于传统单一模型与双LSTM、LSTM-SVR、GM(1,1)-BP等组合模型,LSTM-RELM模型具有更精确的预测效果与更快的运行速度,能为相关销售企业提供广告投放策略参考与库存优化建议。With the development of the Internet,content marketing has gradually become the mainstream of e-commerce marketing,and the daily gross merchandise volume(GMV)is directly related to the inventory optimization control and advertising strategy of enterprises.In order to improve the prediction accuracy,based on the real e-commerce order data set,according to the content marketing index,the influence of user behavior on GMV is analyzed,and a combination model of long short-term memory network(LSTM)and regularized extreme learning machine(RELM)is proposed.The experimental results show more accurate prediction and faster running speed of the LSTM-RELM model proposed in this paper,compared with the traditional machine learning models and combination models,such as LSTM-LSTM,LSTM-SVR,GM(1,1)-BPNN.The model can provide reference for advertising strategy and inventory optimization suggestions for relevant sales enterprises.

关 键 词：长短期记忆网络(LSTM) 极限学习机(ELM) GMV预测 组合预测 

分 类 号：TP389.1[自动化与计算机技术—计算机系统结构]