Stock Price Prediction Based on Gated Recurrent Unit-Attention Mechanism Model
This research constructs a stock price prediction model based on Gated Recurrent Unit-Attention Mechanism(GRU-AM)considering the characteristics of high noise and non-linearity of stock price.The researchers first input the processed stock data into the Gated Recurrent Unit(GRU)to fully learn the data and mine deeper data features,and then introduced the feature vectors learned from GRU into the Attention Mechanism(AM)to learn the weights of different time feature states for capturing the importance of time features more effectively.This research fi-nally verified the effectiveness of the model by comparing with seven baseline models on multiple datasets.
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