基于并联ResNet-GRU模型的股票价格预测

Stock Price Prediction Based on Parallel ResNet-GRU Model

针对目前股价预测模型在随着网络层数增加而导致梯度发生极端变化的问题,提出一种并联残差神经网络(ResNet)和门控循环单元(GRU)网络模型对股票数据的收盘价进行预测。在沪深300股票价格数据上的实验结果表明,该模型的平均绝对误差(MAE)为6.714,均方根误差(RMSE)为60.961,预测误差均低于其他模型;且决定系数(R2)为99.472%,表明预测值和真实值较为接近。该模型对股票价格预测的效果较好。

Aiming at the problem that the current stock price prediction model changes the gradient extremely as the number of layers of the network increases, a parallel ResNet and GRU network model are proposed to predict the closing price of stock data. Experimental results on the CSI 300 stock price data show that the Mean Absolute Error(MAE) of the model is 6.714, the Root Mean Square Error(RMSE)is 60.961, and the prediction error is lower than that of other models. And the coefficient of determination(R~2) is 99.472%, which indicates that the predicted value and the true value are relatively close. The model has a better effect on stock price prediction.