Financial Forecasting with Deep Learning Models Based Ensemble Technique in Stock Market Analysis

In recent years, deep learning techniques have emerged as powerful tools for analyzing and predicting complex patterns in sequential data across various fields.This study employs an ensemble of advanced deep learning models: Long Short-Term Memory (LSTM), Bi-Directional LSTM, Gated Recurrent Unit (GRU), LSTM Convolutional Neural Network (CNN), and LSTM with Self-Attention, to enhance prediction accuracy in time series forecasting.These models are applied to three distinct financial datasets: Tata Motors, HDFC Bank, and INFY.NS, we conduct a thorough comparative analysis to assess their performance.Utilizing K-fold cross-validation, we convert loss (MSE) into RMSE and MAPE, which help estimate accuracy .weachieved train accuracies of 97.46% for Tata Motors, 75.93% for INFY.NS, and 56.60% for HDFC Bank.Our empirical results highlight the strengths and limitations of each model within the ensemble framework and provide valuable insights into their effectiveness in capturing complex patterns in financial time series data.This research underscores the potential of deep learning-based ensemble techniques for improving stock price forecasting and offers significant implications for investors and the development of sophisticated trading and risk management systems.