Stock Price Forecasting through Sequential Models Enhanced with Dual-stage Attention and Denoising

Predicting stock prices is challenging due to the market’s inherent volatility and complexity. This paper explores how sequential models like Recurrent Neural Networks, Long Short-Term Memory, and Gated Recurrent Units can be used for stock price forecasting. To enhance prediction accuracy, Wavelet transform techniques are applied to clean the data from noise. Additionally, a dual-stage attention mechanism is introduced. This mechanism helps in capturing long-term dependencies and focusing on the most relevant data for prediction. In the initial stage, the input attention mechanism determines the most significant features at each time step by referencing the previous encoder hidden state. In the subsequent stage, the temporal attention mechanism identifies the most pertinent hidden states from across all time steps. This two-stage attention approach enhances both the accuracy of predictions and the interpretability of the model. The models are tested on various stock indices, demonstrating that combining data denoising with attention mechanisms significantly enhances forecasting accuracy.