Research on AI Forecasting Model of Logistics Demand Driven by Deep Learning

Logistics demand forecasting plays a vital role in modern logistics management. Traditional demand forecasting methods, such as time series analysis and regression models, are effective in some simple scenarios, but are difficult to cope with complex demand fluctuations and multi-dimensional external influencing factors. To this end, this paper proposes a multilayer temporal convolutional neural network (ML-TCNN) model based on deep learning. The model integrates time series convolution and LSTM networks, which can capture short-term and long-term demand fluctuations and improve the prediction accuracy through the effective integration of external influencing factors (such as weather, holidays, promotions, etc.). This paper uses the demand data of a large logistics company in the past three years, combined with external factors such as weather data and holiday information, to compare the performance of the ML-TCNN model with traditional time series methods (such as ARIMA, SARIMA) and other deep learning methods (such as LSTM, GRU). Experimental results show that the ML-TCNN model outperforms traditional methods in multiple test scenarios, especially when demand fluctuations are large and external factors have a significant impact, showing higher prediction accuracy. Specifically, the ML-TCNN model outperforms other models in MSE and MAPE, and the prediction accuracy is improved by more than 20%.