Stock Price Prediction Model Based on Dual Attention And Temporal Convolutional Network
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 pre-diction model based on dual attention mechanism and temporal convolutional network(TCN)is proposed.First,a con-volution network that is more suitable for time series is used as the feature extraction layer,and feature attention is in-troduced to dynamically mine the potential correlation between the input factor features and closing prices,and sec-ond,on the basis of Gated Recurrent Unit(GRU)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 char-acteristics and time.
Time convolution networkGRUTemporary attentionFeature attentionInterpretability
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