Demand Forecasting of Supply Chain Based on Support Vector Regression Method

Abstract

Introducing the basic theory and computing process of time series forecasting based on Support Vector Regression (SVR) in details, optimizing the parameters of SVR by Genetic Algorithm (GA). Applying SVR to forecast the demand of supply chain in real data, and compared to the RBF neural network method. The result shows that SVR is superior to RBF in prediction performance. And SVR is the suitable and effective method for demand forecasting of supply chain.