Forecast Model of V-SVR Based on an Improved GA-PSO Hybrid Algorithm

Author

Li-Chun Tang ; Xiu-juan Xu ; Liang Lu

Author_Institution

Sch. of Bus. Adm., South China Univ. of Technol., Guangzhou, China

fYear

2012

fDate

2-4 Nov. 2012

Firstpage

725

Lastpage

728

Abstract

This paper firstly puts forward a new improved GA-PSO algorithm, which will solve the local and global contradictions on optimization better and can ensure the diversity, simplicity and efficiency of the population of particles at the same time. Then we embed it into an improved support vector machine (V-SVR) forecasting model, with parameters adaptive and different type of sample input feasible. In the end, this paper use matlab09a and the data of GDP, GZII and EN for model training and forecast simulation, and make comparison of results with RBF, PSO-V-SVR and GA-V-SVR model. It shows that the improved GA-PSO based on V-SVR model has the most powerful forecast capability.

Keywords

economic indicators; forecasting theory; genetic algorithms; investment; particle swarm optimisation; support vector machines; EN data; GDP data; GZII data; Guangzhou infrastructure investment; Matlab09a; V-SVR forecast model; adaptive parameters; energy demand data; forecast capability; forecast simulation; genetic algorithm; gross domestic product; improved GA-PSO hybrid algorithm; improved support vector machine forecasting model; model training; particle swarm optimization; Algorithm design and analysis; Genetic algorithms; Mathematical model; Optimization; Predictive models; Sociology; Statistics; V-SVR; Genetic Algorithms; Particle swarm optimization; Forecast model;

fLanguage

English

Publisher

ieee

Conference_Titel

Multimedia Information Networking and Security (MINES), 2012 Fourth International Conference on

Conference_Location

Nanjing

Print_ISBN

978-1-4673-3093-0

Type

conf

DOI

10.1109/MINES.2012.114

Filename

6407403