CNN-GRUA-FC Stock Price Forecast Model Based on Multi-Factor Analysis
Shuying Yang, Haiming Guo, and Junguang Li
School of Computer Science and Engineering, Tianjin University of Technology
No.391 Bin Shui Xi Dao Road, Xiqing District, Tianjin 300384, China
To predict stock prices, this paper proposes a CNN-GRUA-FC model based on multi-factor analysis for time series forecasting. First, we use the random forest algorithm to evaluate the importance of the factor series, selecting the factor series of greater importance as the input of the subsequent prediction model. We then use the convolutional neural network (CNN) to extract the spatial characteristics of stock data for prediction, taking advantage of the gated recurrent unit (GRU) neural network to extract the dynamic characteristics of stock data and make prediction. Additionally, we combine an attention mechanism with the GRU neural network (GRUA) to improve its prediction performance. Finally, the prediction results of the two different sub-models are passed through the fully connected (FC) layer to obtain the final predictions. The results show that the prediction accuracy of the CNN-GRUA-FC prediction model proposed in this paper is higher than that of other models.
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