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@article{177316,
        author = {T. Renuka and B. Bhagya Lakshmi},
        title = {Bidirectional LSTM Model for Dynamic Bitcoin Price Prediction in Cryptocurrency Markets},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {636-641},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177316},
        abstract = {Cryptocurrency markets, especially Bitcoin, exhibit dynamic and volatile behavior, posing challenges for accurate price prediction. This research introduces a novel approach, employing Bidirectional Long Short-Term Memory (BiLSTM) networks to forecast Bitcoin prices. The Bidirectional LSTM model captures temporal dependencies in both directions, enhancing understanding of underlying patterns in historical price data. We utilize a curated dataset of Bitcoin prices for training, incorporating preprocessing steps to handle missing data and normalize inputs. Systematic hyperparameter optimization fine-tunes the Bidirectional LSTM architecture for improved predictive performance. Evaluation metrics, such as Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), assess the model's accuracy and generalization capabilities. Results reveal the superiority of the proposed Bidirectional LSTM model over traditional unidirectional LSTM models and baseline methods. This research contributes valuable insights to the potential of deep learning for cryptocurrency price prediction, contributing to the predictive modeling literature in financial markets. It also establishes a foundation for exploring advanced neural network architectures in cryptocurrency analytics.},
        keywords = {Bitcoin Prediction, Bidirectional LSTM, Cryptocurrency, Machine Learning, Financial Forecasting.},
        month = {May},
        }