Elevating Stock Market Predictions: An Attention-Infused LSTM Model with Wavelet Denoising

Financial time series data is volatile and noisy, making stock market prediction challenging. Traditional approaches sometimes fail to capture complicated, non-linear patterns in such data, resulting in poor predictions. This research suggests an Attention-driven Long Short-Term Memory model integrated with wavelet denoising using the Coif3 wavelet for predicting the NIFTY 50 index. Our model uses an additive attention technique to dynamically focus on important time steps, enhancing predictions. To find the best predictors, we used Recursive Feature Elimination (RFE) with a Random Forest Regressor on 38 historical and technical features. Wavelet transform denoising decreases noise, making data better for the LSTM model. The model is tested for RMSE, MAE, and R2, showing greater predictive power. Additionally, an ablation study critically evaluates wavelet denoising and the attention mechanism, showing that their combination improves prediction accuracy.