Title :
Car Sales Volume Prediction Based on Particle Swarm Optimization Algorithm and Support Vector Regression
Author :
Lu, Xiaoyong ; Geng, Xiaomeng
Author_Institution :
Sch. of Econ. & Manage., Nanchang Univ., Nanchang, China
Abstract :
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.
Keywords :
particle swarm optimisation; prediction theory; regression analysis; support vector machines; MAPE; PSO; SVR; car sales volume prediction; global search capability; mean absolute percentage error; particle swarm optimization algorithm; particles swarm optimization algorithm; support vector regression; Biological system modeling; Computational modeling; Mathematical model; Particle swarm optimization; Prediction algorithms; Predictive models; Support vector machines; PSO; PSO-SVR; Particle Swarm optimization; SVM; SVR; Support Vector Machine;
Conference_Titel :
Intelligent Computation Technology and Automation (ICICTA), 2011 International Conference on
Conference_Location :
Shenzhen, Guangdong
Print_ISBN :
978-1-61284-289-9
DOI :
10.1109/ICICTA.2011.25
