A Combined Model for INDEX Price Forecasting Using LSTM, RNN, and GRU

Stock price prediction has always been a challenging task due to its inherent complexity and unpredictability. Traditional methods often struggle to capture the dynamic patterns and nonlinear relationships in financial data, leading to limited accuracy in forecasting. In recent years, deep learning models, such as “Long Short-Term Memory,” “Gated Recurrent Unit,” and “Recurrent Neural Network,” have demonstrated encouraging outcomes in a number of time series prediction challenges. In this study, we propose a novel approach that combines the strengths of LSTM, RNN, and GRU models to enhance stock price prediction accuracy. Our model utilizes the LSTM’s ability to capture long-term dependencies, RNN’s capability to process sequential data, and GRU’s efficiency in learning from large-scale datasets. By integrating these three architectures, our model effectively addresses the limitations of individual models and provides a comprehensive framework for stock price forecasting. To determine if our recommended model is effective, we perform research on a BANKNIFTY and NIFTY50 dataset of historical stock prices. We compare the results with traditional models, standalone LSTM, RNN, and GRU models, as well as other state-of-the-art deep learning models for index price prediction. The experimental results demonstrate the superiority of our combined model in terms of prediction accuracy, robustness, and adaptability to different market conditions.