Stock Price Forecasting Using A Hybrid ARMA and BP Neural Network and Markov Model

被引:0
|
作者
Shi, Shuzhen [1 ]
Liu, Wenlong [1 ]
Jin, Minglu [1 ]
机构
[1] Dalian Univ Technol, Sch Informat & Commun Engn, Dalian, Peoples R China
来源
PROCEEDINGS OF 2012 IEEE 14TH INTERNATIONAL CONFERENCE ON COMMUNICATION TECHNOLOGY | 2012年
关键词
stock price forecasting; ARMA; BPNN; Markov model; ARIMA;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Stock price forecasting is a very important financial topic and it is of great importance to both market economy and investors. Stock price series is complex, nonlinear and dynamic that it's difficult to predict it effectively by a single method. This paper proposes a hybrid method combining autoregressive and moving average (ARMA), back propagation neural network (BPNN) and Markov model to forecast the stock price. ARMA and BPNN solve the linear and nonlinear component of the stock price series respectively and Markov model can modify the result to be better. The experimental result shows that the proposed method can improve forecasting accuracy.
引用
收藏
页码:981 / 985
页数:5
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