Stock Market Price Prediction Using GRU and XGB.

Predicting the future course of the stock market is challenging because of its volatility and multifaceted dependencies. This study introduces a novel approach that harnesses the power of Deep Learning Recurrent Neural Network (RNNs), including established models like LSTM, Bidirectional LSTM, GRU, and XGBOOST, with a unique focus on improving prediction accuracy. By integrating historical data and technical indicators, such as Moving Averages, our framework refines prediction strategies. Our key innovation lies in the strategic combination of GRU, specialized for capturing long-term trends, and XGBoost, which generates final forecasts with remarkable precision. This novel approach enhances the overall strategy and substantially increases forecast accuracy, making a significant contribution to the field of stock market prediction.