摘要
股票价格通常受市场各种因素的影响,并且在价格波动上通常表现出非线性和不确定性。在解决股票价格预测问题时,由于单一预测方法自身的局限性,往往准确度较低。因此,为了获取更加准确的预测结果,有必要结合2种或者更多的预测方法,建立一种组合预测模型。因此,本文提出了基于GM-RBF神经网络的股票价格预测模型,实验结果表明,相对单一的预测模型,GM-RBF神经网络的股票价格预测模型能够更加精确地对股票价格进行预测,更加客观地反映股票价格变化的规律。
Stock price is usually influenced by various factors in the market,and usually shows nonlinearity and uncertainty in price fluctuation. When we solve the stock price prediction problem,the accuracy of the single prediction method is often low due to its limitations. Therefore,in order to obtain more accurate prediction results,it is necessary to combine two or more prediction methods to establish a combination forecasting model. In view of stock price forecasting,the stock price prediction model based on GM-RBF neural network is proposed. The experimental results show that the stock price prediction model of GM-RBF neural network can predict the stock price more accurately and reflect the law of stock price change more objectively.
作者
刘述忠
LIUShu-zhong(Business School,Hohai University,Nanjing 211100,China)
出处
《计算机与现代化》
2018年第8期8-11,共4页
Computer and Modernization
关键词
股票价格
灰色算法
神经网络
预测模型
stock price
grey algorithm
neural network
prediction model
