Cryptocurrency resembles the eminent key elements in the progress of financial and economic domains, which enables safe and secure money transactions. However, these currencies are not preferred as an investment prospect because of the price volatility and the unpredictable market price. However, the existing techniques fail to learn the uncertainty and fluctuations in their prices limiting their applicability for real-time price prediction. Consequently, this research proposes the Coua Porteles-Bidirectional Long Short-Term Memory (CP-BiLSTM) model for accurate prediction of cryptocurrency price from the historical price data. The proposed CP-BiLSTM model effectively captures the intricate patterns hidden in the multivariate time-series data and facilitates precise cryptocurrency price prediction. Specifically, the Coua Proteles algorithm fine-tunes the hyperparameters of the classifier and aids in improving the CP-BiLSTM model’s performance. Ultimately, the Coua Porteles optimization helps to reduce the error function and enhance the efficiency of the classifier in predicting the cryptocurrency price. The experimental results demonstrate that the proposed CP-BiLSTM approach achieves superior results attaining the MAE, MSE, RMSE, and R2-score of 2.49, 2.41, 1.55, and 0.70 respectively outperforming the other existing techniques in cryptocurrency price prediction.
Mahbub Arab Majumder (Mon,) studied this question.