Deep Learning Approaches for Stock Price Prediction: A Comparative Study of LSTM, RNN, and GRU Models

Stock price prediction has been a longstanding challenge in financial markets, and recent advances in deep learning techniques have spurred increased interest in developing accurate and reliable models for forecasting. The comparative study is conducted using historical stock price data from diverse financial markets and periods. The dataset is preprocessed to address issues such as non-linearity, volatility, and noise. Subsequently, the LSTM, RNN, and GRU models are implemented and fine-tuned to optimize their performance in predicting stock prices. Performance metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) are employed to evaluate and compare the predictive accuracy of each model. Additionally, the study investigates the models' abilities to capture long-term dependencies and adapt to changing market conditions. The results of the comparative analysis provide insights into the relative strengths and weaknesses of LSTM, RNN, and GRU models for stock price prediction. The findings contribute to the ongoing discourse on the applicability of deep learning techniques in financial forecasting and offer practical recommendations for researchers and practitioners seeking to enhance the precision of stock price predictions. In conclusion, the study not only contributes valuable insights into the relative strengths and weaknesses of LSTM, RNN, and GRU models for stock price prediction but also emphasizes the practical utility of the predictions by visually displaying the differences through charts and graphs. This dual approach advances the understanding of deep learning techniques in financial forecasting and provides actionable recommendations for researchers and practitioners seeking to enhance the precision of stock price predictions.