Research on Stock Prediction Based on Bidirectional Long Short Term Memory Neural Network Model Based on Attention Mechanism Optimization
DOI:
https://doi.org/10.54097/hbem.v10i.8112Keywords:
Attention mechanism, BiLSTM model, CNN model, Neural networks, Stock forecasts.Abstract
The complexity and volatility of financial markets place higher demands on the accuracy of stock forecasts. This paper uses Ping An Daily Market Data as the source of experimental data for empirical analysis, and innovatively combines BiLSTM with attention mechanism for stock price prediction in the experimental process. Introduce an attention mechanism, AM, to reinforce important features by reassigning the weight of each data; Bidirectional LSTM can learn sequential and reverse time series data information to ensure adequate data utilization. After establishing BP (backpropagation), LSTM and CNN-LSTM models for comparison, it is found that the proposed AM-BiLSTM model has high accuracy, good feasibility and versatility.
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