Logistics Demand Forecasting and Simulation Based on Support Vector Machine

The need for regional logistics has increased dramatically due to the logistics industry's rapid development, underscoring the need for accurate forecasting. The deployment of a precise logistics demand forecasting model has the potential to mitigate expenses, augment logistics efficacy, and furnish direction for forthcoming logistics sector planning. This research suggests an approach based on the Support Vector Machine (SVM) model to increase the accuracy of logistics demand forecasting in light of the complexity of regional logistics demand forecasting. First, in order to determine the primary factors influencing regional logistics demand, grey relational analysis is utilized. Subsequently, the sample data is input, and the Support Vector Machine model is utilized for learning, establishing a relationship model between regional logistics demand and influencing indicators. The predictions produced by a Back Propagation (BP) neural network are then contrasted with this. Lastly, data on logistics demand from Jiangsu Province covering the years 2004 to 2022 is used for simulation study. The results demonstrate that the Support Vector Machine performs better in regional logistics demand forecasting when compared to the BP neural network. The studys findings support the advancement of scientific demand forecasting for regional logistics, offer a vital basis for decision-making in logistics system design, and strengthen the areas potential for sustainable growth.