Performance Analysis of LSTM, Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), Convolutional LSTM (ConvLSTM) and LSTM with Attention in Stock Market Prediction

  • Authors

    • Santosh Kumar Sahu Department of Computer Science and Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology (VNIT), Nagpur, India Engineering, Visvesvaraya National Institute of Technology (VNIT), Nagpur, India and
    • A. S. Mokahde Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology (VNIT), Nagpur, India
    • Jagdish Chakole Department of Computer Science and Engineering, Indian Institute of Information Technology (IIIT), Nagpur, India
    • Samir N. Ajani School of Computer Science and Engineering, Ramdeobaba University (RBU), Nagpur, India
    • Manish M. Goswami Department of Computer Science and Engineering, SB Jain Institute of Technology, Management and Research, Nagpur, India
    https://doi.org/10.14419/6wpxv706

    Received date: June 6, 2025

    Accepted date: June 18, 2025

    Published date: July 27, 2025

  • Deep Learning, Attention Mechanisms, Recurrent neural networks, LSTM, GRU, BiLSTM, Stock Market

  • Abstract

    Predicting the stock market is a complex and challenging endeavor because financial time series are inherently volatile and nonlinear. Recurrent neural networks (RNNs) and their advanced variants have shown significant promise in modeling sequential data. This study evaluates and contrasts the effectiveness of five deep learning models Classic LSTM, Bidirectional LSTM (BiLSTM), GRU, Convolutional LSTM (ConvLSTM), and LSTM with Attention Mechanism in forecasting stock market closing prices. Our evaluation employs historical stock index data, with model performance quantified using standard regression metrics—MSE, RMSE, MAE, MAPE, and R². Our results show that GRU consistently outperforms other models in capturing intricate temporal patterns, Results show that LSTM with Attention Mecha-nism achieves the highest accuracy (MSE: 0.000370 MAE: 0.014482, RMSE: 0.019242, MAPE: 6.676856 R2: 0.873357) while GRU offers the best balance of accuracy and efficiency. These findings provide actionable insights for selecting appropriate RNN models for financial forecasting applications.

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  • How to Cite

    Sahu , S. K. ., Mokahde , A. S. ., Chakole , J. ., Ajani , S. N. ., & Goswami , M. M. . (2025). Performance Analysis of LSTM, Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), Convolutional LSTM (ConvLSTM) and LSTM with Attention in Stock Market Prediction. International Journal of Basic and Applied Sciences, 14(SI-2), 131-140. https://doi.org/10.14419/6wpxv706