FS-LSTM:sales forecasting in e-commerce on feature selection
FS-LSTM: sales forecasting in e-commerce on feature selection作者机构:Business SchoolJinhua PolytechnicJinhua 321017China School of Electronic EngineeringBeijing University of Posts and TelecommunicationsBeijing 100876China
出 版 物:《The Journal of China Universities of Posts and Telecommunications》 (中国邮电高校学报(英文版))
年 卷 期:2022年第29卷第5期
页 面:92-98页
核心收录:
学科分类:12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0714[理学-统计学(可授理学、经济学学位)] 0835[工学-软件工程] 0701[理学-数学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术(可授工学、理学学位)]
主 题:sales forecasting time series forecasting deep learning feature selection
摘 要:There are many studies on sales forecasting in e-commerce,most of which focus on how to forecast sales volume with related e-commerce operation *** this paper,a deep learning method named FS-LSTM was proposed,which combines long short-term memory(LSTM)and feature selection mechanism to forecast the sales *** indicators with most contributions by the extreme gradient boosting(XGBoost)model are selected as the input features of LSTM ***-LSTM method can get less mean average error(MAE)and mean squared error(MSE)in the forecasting of e-commerce sales volume,comparing with the LSTM model without feature *** results show that the FS-LSTM can improve the performance of original LSTM for forecasting the sales volume.