A COMPARATIVE STUDY OF DEEP LEARNING MODELS FOR STOCK PRICE PREDICTION
DOI:
https://doi.org/10.29121/granthaalayah.v13.i4.2025.6176Keywords:
Lstm, Bi-Lstm, Gru, Cnn-Lstm, Cnn-Gru, Stock Market Prediction, Deep LearningAbstract [English]
This research investigates the effectiveness of five advanced deep learning models—LSTM, Bi-LSTM, CNN-LSTM, GRU, and CNN-GRU—in forecasting stock prices. By leveraging historical stock data from Yahoo Finance, we implement and evaluate each model based on prediction accuracy, RMSE, MSE, and R². The study includes detailed preprocessing steps, model architecture explanations, hyperparameter tuning, visual performance comparisons, and result analysis. The RMSE for all the introduced models was measured by varying the number of epochs, Our findings show that while all models offer valuable predictive power, hybrid architectures such as CNN-GRU outperform others in terms of accuracy and generalization. This comprehensive evaluation can guide future research and practical deployment of deep learning techniques in financial forecasting.
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Copyright (c) 2025 Ayush Rajbhar, Rahul Kumar Gupta, Aman Chaudhary, Abhishek Kumar, Ranjeet Kumar Dubey
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