基于扩展记忆的粒子群优化算法（Particle Swarm Optimization based on memory）-This paper combines SVM with improved PSO (Particle Swarm Optimization with Extended Memory, PSOEM) and then builds PSOEM-SVM forecasting model. The PSOEM searches the solution space intelligently and finds out the best one. Parameters in SVM are optimized by PSOEM, which implements automation of the parameter optimization avoiding the blindness of selecting parameters. Not only does it utilize the generalization feature of SVM, but enhance the global search ability of PSO (Particle Swarm Optimization). Thus, both accuracy and speed are increased at the same time.