A Comprehensive Approach to Stock Price Prediction using Dual LSTM Models

This paper introduces an unique approach to predicting stock prices using two LSTM models. The first model is trained on past stock prices, while the second model learns from the difference between the stock prices the first model predicts and the actual prices. The final predicted stock price is obtained by combining the predicted price and predicted error. Our model demonstrates significant improvements in forecasting accuracy compared to existing models.