Research on stock price prediction based on EWT and ESN models
In order to improve the prediction accuracy of stock price,this paper proposes a combination prediction method of stock price data based on empirical wavelet transform,particle swarm optimization algorithm and echo state network model.The basic framework of the data prediction model is as follows.Firstly,the signal decomposition algorithm is used to decompose the stock price data into different intrinsic mode components.The phase space of the intrinsic mode components is reconstructed and the particle swarm optimization algorithm is used to optimize the echo state network to extract the depth features of each mode component and predict it.Finally,the predicted values of each component are integrated to obtain the final predicted value.In order to better measure the feasibility and efficiency of the proposed method,three stock price series of China Securities 500,Kweichow Moutai and Sany Heavy Industry were used to evaluate the accuracy of the model.The experimental results show that the proposed method has higher prediction accuracy of stock prices than other prediction models.
stock price predictionempirical wavelet transformecho state networkcomposite pattern
NETL
NSTL
万方数据
维普
