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 data.In this paper,a deep learning method named FS-LSTM was proposed,whic...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 data.In 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 volume.The indicators with most contributions by the extreme gradient boosting(XGBoost)model are selected as the input features of LSTM model.FS-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 selection.The results show that the FS-LSTM can improve the performance of original LSTM for forecasting the sales volume.展开更多
文摘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 data.In 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 volume.The indicators with most contributions by the extreme gradient boosting(XGBoost)model are selected as the input features of LSTM model.FS-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 selection.The results show that the FS-LSTM can improve the performance of original LSTM for forecasting the sales volume.
