关键词: 股指预测, 卷积神经网络（CNN）, 门控循环单元神经网络

Abstract:

Aiming at the collinear and nonlinear characteristics of stock data, a hybrid forecasting model based on Convolutional Neural Network（CNN） and Gated Recurrent Unit（GRU） neural network is proposed to predict CSI 300 Index, SSE Composite Index and SZSE Component Index. Firstly, this model uses CNN to extract feature vectors and reduce the dimension of original data. Then, it utilizes GRU neural network to learn the dynamic changes of features and predict the stock index. The simulation results show that compared with GRU neural network, Long Short-Term Memory（LSTM） neural network and CNN, this model can mine the information contained in historical data, effectively improve the accuracy of the stock index forecasting, and provide some reference value for the stock index trading.

Key words: stock index forecasting, Convolutional Neural Network（CNN）, gated recurrent unit neural network