摘要
现实中的金融时间序列存在非线性、不确定性等特点,利用传统的预测方法难以获得满意的结果。提出了一种基于PSO优化LSSVM模型参数的股价时间序列预测方法。利用PSO算法的收敛速度快和全局收敛能力,优化LSSVM的惩罚因子和核函数参数。利用该方法应用于金融市场中的股价序列预测,与传统方法对比表明,该模型能够提高金融时间序列的预测精度,其具有更好的泛化能力,这对国内投资者进行股票投资具有现实的借鉴意义。
Because of the nonlinear and uncertain characteristics of the financial time series,it is difficult to obtain satisfactory results by using traditional forecasting methods This investigation proposes a method to predict the stock price time series based on the LSSVM model which uses PSO algorithm to optimize its parameters The PSO algorithm is used to optimize the penalty factor and kernel function parameter of LSSVM with fast convergence speed and global convergence ability This proposed model can improve the prediction accuracy of financial time series and has better generalization ability when using it to predict time series of stock price in financial market compared with the traditional methods
作者
王国俊
WANG Guo-jun(Business School,University of Shanghai for Science and Technology,Shanghai 200093,China)
出处
《科技和产业》
2017年第10期132-137,共6页
Science Technology and Industry
关键词
时间序列预测
最小二乘支持向量机
ARIMA模型
交叉验证
粒子群算法
time series forecasting
least squares support vector machine
arima model
cross validation
particle swarm optimization
