Yifeng Fu and He Xiao, Jiangxi University of Science and Technology, China
The stock market is affected by many variables and factors, and the current forecasting models for time series are often difficult to capture the complex laws among multiple factors. Aiming at this problem, a stock price prediction model based on dual attention mechanism and temporal convolutional network is proposed. First, a convolution network more suitable for time series is used as the feature extraction layer. Feature attention is introduced to dynamically mine the potential correlation between the input factor features and closing prices. Second, based on Gated Recurrent Unit, on the other hand, a temporal attention mechanism is introduced to improve the model's ability to learn important time points and obtain importance measures from a temporal perspective. The experimental results show that the proposed model performs better than the traditional prediction model in the error index of stock price prediction and realizes the interpretability of the model in terms of index characteristics and time.
Time convolutional network, GRU, Temporary attention, Feature attention, Interpretability.