FS-LSTM:sales forecasting in e-commerce on feature selection

作     者：Zhang Han Jing Yinji Zhao Yongli Zhang Han;Jing Yinji;Zhao Yongli

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.