A comparative evaluation of five state-of-the-art machine learning models, such as Temporal Fusion Transformer (TFT), Temporal Convolutional Network (TCN), XGBoost, Random Forest, and a CNN-LSTM hybrid, for forecasting cryptocurrency price volatility across major trading pairs shows that TFT achieves the lowest test loss and highest classification accuracy, outperforming other architectures.
A comparative analysis of machine learning and deep learning models for cryptocurrency price forecasting using historical Aave (AAVE) cryptocurrency data indicates that deep learning models, especially LSTM, are more suitable for capturing temporal dependencies and nonlinear patterns in cryptocurrency price data.
Rukhsar Zaka, Faiza Irfan, Sidra Rehman et al.· International Journal of Adv...· 0 citations
This project presents an intelligent and data-driven approach for cryptocurrency analysis using Long ShortTerm Memory (LSTM) networks, a specialized type of Recurrent Neural Network (RNN) which enables the model to learn temporal dependencies across sequential data.
D. Rahul, M. Shiva, Parag Ravikant Kaveri et al.· International Journal of Inn...· 0 citations
This study aimed to develop and evaluate a hybrid deep ensemble learning framework integrating CNN-LSTM, GRU, and Transformer architectures through stacking for accurate next-day cryptocurrency price prediction. This quantitative predictive study analyzed daily market data for five major cryptocurrencies, including Bit...
Mohammadreza Haghighi, Seyed Yashar Banihashem, Mohammadmehdi Gilaniansadeghi· Journal of Management and Bu...· 0 citations
This work attempts to provide a thorough comparative analysis mapping the precise accuracy–efficiency trade-off between Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models under a standardized Grid Search hyperparameter optimization pipeline using a recent Bitcoin closing-price dataset.
A methodological framework based on LSTM neural networks for comparing the performance of various sets of features in predicting Bitcoin prices and proof that technical indicators have a significant impact on the predictive accuracy of this model, while social media sentiment has a minor impact under the circumstances...
Sedeeq Hasan Banna, Ammar Ahmed Othman, Khaled Al-Raddah· SISTEMASI· 0 citations
A hybrid forecasting framework that integrates sentiment analysis with deep learning to predict Bitcoin’s hourly and daily closing prices and empirical results demonstrate that sentiment-enhanced hybrid models consistently outperform models based solely on technical indicators across RMSE, MAE, MAPE, and R² metrics.
Meltem Kavaklı, Kadriye Filiz Balbal· Balıkesir Üniversitesi Fen B...· 0 citations
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