山东农业大学学报（自然科学版）Issue(4)：628-631,4.DOI:10.3969/j.issn.1000-2324.2015.04.031
基于改进的粒子群算法优化LSSVM股价预测研究
Study on the Prediction for Stock Price Based on the Optimized LSSVM of the Improved Particle Swarm Algorithm
刘家旗1
作者信息
- 1. 西北大学经济管理学院，陕西 西安 710127
- 折叠
摘要
Abstract
In order to improve the prediction accuracy of the stock price, stock price data for the nonlinear and non-stationary characteristics, this paper used the improved PSO to implement the self-adaptive selection of the LSSVM kernel parameter and penalty coefficient, and proposed a prediction model for stock price on SAPSO optimized LSSVM to analyze a case. The results showed that it had the high prediction accuracy, the advantages of short time and also could realize the self-adaptive selection for forecasting parameters based on prediction results in 1 step, 3 step, 5 step and the 7 on the SAPSO-LSSVM algorithm and the comparison between prediction time and the mean square error of different models.关键词
粒子群算法/股票预测/LSSVMKey words
Particle Swarm Algorithm/prediction for stock price/Least Squares Support Vector Machine(LSSVM)分类
管理科学引用本文复制引用
刘家旗..基于改进的粒子群算法优化LSSVM股价预测研究[J].山东农业大学学报（自然科学版）,2015,(4):628-631,4.
