E-commerce sales forecasting based on deep learning algorithm

With the rapid development of e-commerce, accurate sales forecasting has become the core technology demand for enterprises to optimize inventory management and avoid the risk of supply and demand imbalance. Aiming at the limitations of traditional forecasting methods in dealing with nonlinear relations and unexpected events, this study proposes an improved multi-layer long short-term memory network (LSTM) model. By introducing temporal feature decoupling mechanism and dynamic regularization strategy, a sales forecasting system for complex electronics market landscape is constructed. At the model design level, a three-layer LSTM network overlay architecture is adopted, combined with Dropout pool layer (0.3 zero setting rate) and L2 regularization method to suppress overfitting. In the optimization process, the stochastic gradient descent algorithm and Amad adaptive learning rate (0.1 initial value) are used to effectively balance the training efficiency and the stability of parameter convergence. The research shows that the model provides a practical technical solution for e-commerce enterprises to achieve accurate matching of supply and demand. This study not only promotes the engineering application of deep learning in the field of sales forecasting, but also provides a methodological framework with both theoretical innovation and practical value for supply chain decision-making in a dynamic market environment.