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

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. © 2022, Beijing University of Posts and Telecommunications. All rights reserved.