Car Sales Volume Prediction Based on Particle Swarm Optimization Algorithm and Support Vector Regression

In this paper, the car sales prediction model is established by using Support Vector Regression (SVR) combined with Particles Swarm Optimization algorithm (PSO-SVR). In this model, PSO Algorithm is used to optimize the 3 parameter used in Support Vector Regression. PSO algorithm not only has a strong global search capability, but also solved the problem of over-fitting. Moreover, Mean Absolute Percentage Error(MAPE) is used to measure the error between predict value and actual value. The experimental result shows that the PSO-SVR model is superior to GA-SVR model in the running efficiency and predict accuracy.