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
The high volatility and complexity of cryptocurrency markets create difficulties for investors and researchers who
attempt to make accurate price predictions. 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. The system builds predictive models
through training which uses historical price data that includes opening price, closing price, high, low, and trading volume.
The proposed model follows a structured workflow that includes data collection from financial APIs, preprocessing
techniques such as normalization and time-series windowing, and model training using LSTM architecture. The LSTM
model enables accurate predictions through its ability to learn long-term dependencies and patterns which exist in
cryptocurrency price movements. The evaluation process uses Root Mean Square Error (RMSE) and Mean Absolute Error
(MAE) metrics to test prediction accuracy. The system includes a core function which displays actual versus predicted price
data through graphical visualizations to assist users in understanding market patterns and model effectiveness. The
approach provides better interpretability which helps traders and analysts to make better decisions. The system supports
scalability which allows its application to various cryptocurrencies including Bitcoin and Ethereum and other digital
currencies.
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 Bitcoin, Ethereum, Binance Coin, Ripple, and Cardano, over the period from January 1, 2018, to December 31, 2025. The dataset comprised 14,610 cryptocurrency-day observations, with 2,922 observations for each asset. Data were chronologically divided into training, validation, and testing subsets. Historical price, trading volume, return, volatility, lagged variables, and technical indicators were used as predictors. A 30-day sliding window was employed to predict the following day’s closing price. The proposed framework combined CNN-LSTM, GRU, and Transformer models using a stacking-based meta-learner. Performance was evaluated using Mean Absolute Error, Root Mean Squared Error, Mean Absolute Percentage Error, and R². Diebold-Mariano tests were used to compare out-of-sample forecast accuracy. The proposed hybrid ensemble achieved an MAE of 0.0294, RMSE of 0.0436, MAPE of 3.12%, and R² of 0.971, outperforming CNN-LSTM, GRU, Transformer, ARIMA, Random Forest, XGBoost, and persistence benchmarks. Diebold-Mariano tests showed that the ensemble produced significantly lower forecast loss than all competing models, including Transformer, the strongest standalone model, with all p values below 0.001. Cryptocurrency-specific analyses yielded R² values above 0.960 for all five assets, while volatility-regime analysis showed that predictive accuracy declined under high-volatility conditions but remained strong, with an R² of 0.952. The findings demonstrate that combining convolutional, recurrent, and attention-based deep-learning mechanisms through stacking can significantly improve the accuracy and robustness of cryptocurrency price forecasting across heterogeneous assets and changing volatility conditions.
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 are brought.
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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