Triple Attention-Based Hybrid Deep Learning Framework for Enhanced Stock Market Prediction

Abstract

Stock price prediction is a complex problem because financial time series data are volatile and complicated. The model should learn the temporal relationship and complex spatial patterns in data for precise stock price prediction. Conventional methods used for stock price forecasting have many limitations regarding handling nonlinear, complex, and dynamic data. This study assesses a hybrid deep learning model integrated with a triple attention mechanism to predict stock prices. It is experimental that the proposed MTA-HDCRNN model performs well on intricate data. The deep CNN works well on finding the local patterns in the data, whereas the simple RNN supports to learn sequential data. The triple attention mechanism emphasizes which features to focus on and where to focus. The dataset used for analysis is the BSE and Nifty 50. Web scraping is done to get the news data. Feature extraction includes statistical features, entropy features, PCA features, and technical indicators. Overall, the complete architecture of the proposed model is vigorous. It is observed that there is a 2% to 6% decrease in error values when the model is compared with existing state-of-the-art models. Experimentation shows that the proposed model enhances the stock price prediction, making it useful for investors and financial analysts for decision-making.