Stock price prediction based on HDA-LSTM model

To better cope with the volatility of stock prices, this paper proposes an innovative HDA-LSTM (Hierarchical Double Attention-Long short-term model) model, which combines the long and short-term memory network (LSTM) with the attention mechanism and dynamically allocates the attention mechanisms by building a two-layer attention mechanism layer, and dynamically allocates the weights, thereby improving the accuracy and robustness of stock price prediction. In this paper, a dataset of stocks from the Shanghai Stock Exchange (SSE) Small and Medium Index Board (SMB) is selected to predict the closing prices after several days. A comprehensive comparison is made with the traditional machine learning model, and the single-layer LSTM-attention mechanism model. The results indicate that the HDA-LSTM model performs excellently, demonstrating strong generalization and adaptability in handling complex time series analysis. This suggests that the model provides an effective and reliable approach to stock market forecasting.