Research on the Construction of Short-Term Demand Forecasting Model of Express Logistics Based on Deep Learning

With the rapid growth of live e-commerce and new retail, the short-term demand of express logistics presents a high degree of volatility and complexity. Faced with this situation, the traditional forecasting methods of express logistics demand mainly include time series analysis and shallow machine learning methods, which have obvious limitations in dealing with complex nonlinear relational data and capturing data characteristics, and are difficult to meet the practical application requirements. In this regard, based on the current situation, this paper will deeply explore the application feasibility of artificial intelligence and deep learning technology in this field, and put forward a brand-new short-term demand forecasting model for express logistics, aiming at improving the interpretability, accuracy and real-time performance of the forecasting results. Practice has proved that the whole model belongs to mixed deep learning model, and the internal local mean decomposition layer (LMD) can decompose non-stationary time series data and extract multi-scale features. The long-term and short-term memory network (LSTM) can capture the long-term dependence of time series to solve the shortcomings of traditional methods in noise processing, nonlinear modeling and emergency adaptability, and then provide strong support for the operation decision of express logistics enterprises.