E-Commerce Sales Forecasting Algorithm Based on Long Short-Term Memory Network

As a special recurrent neural network, long short-term memory network can effectively capture the longterm dependence relationship in time series data, which is very suitable for processing e-commerce sales data with time series characteristics. This study analyzes the basic principle of LSTM and its advantages in processing time series data, and proposes the construction process of e-commerce sales forecasting model based on LSTM. The experimental results of this study show that the algorithm has shown good forecasting performance on e-commerce sales data sets, and has significantly improved forecasting accuracy and stability compared with traditional forecasting methods.