Advanced Hybrid RNN Architectures for Real-time Cryptocurrency Forecasting and Strategic Trading Optimization - Kehelwala Dewage Gayan Maduranga; Shamima Nasrin Tumpa (Proceedings of the International Florida Artificial Intelligence Research Society Conference)
The cryptocurrency market is characterized by its high volatility and complex temporal dependencies, posing significant challenges for accurate price prediction. This study introduces advanced hybrid Recurrent Neural Network (RNN) architectures—LSTM-GRU, GRU-BiLSTM, and LSTM-BiLSTM—to enhance the predictive accuracy of cryptocurrency price forecasting. By leveraging the strengths of each RNN variant, the hybrid models effectively capture intricate time-series patterns and nonlinear dependencies inherent in cryptocurrency data.