A Novel Hybrid Transformer-Based Deep Learning Approach for Multi-Step Bitcoin Price Forecasting
Received: 3 December 2025 | Revised: 28 December 2025 | Accepted: 6 January 2026 | Online: 9 February 2026
Corresponding author: Rza Hasanli
Abstract
The unpredictable and highly dynamic nature of cryptocurrency markets has driven researchers to develop advanced forecasting techniques that can support decision-making in trading and risk management. This study proposes a hybrid deep learning framework that combines a Transformer with recurrent models for multi-step Bitcoin price forecasting. The model operates on log-differenced closing prices and is evaluated for 7-, 14-, and 21-day ahead prediction using recursive multi-step forecasting schemes. In addition to the proposed Transformer-based architectures, a comprehensive comparison was conducted against four benchmark recurrent models, namely Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), along with their bidirectional variants Bidirectional-LSTM (BiLSTM) and Bidirectional GRU (BiGRU). Model performance was assessed using standard regression metrics, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Directional Accuracy (DA) to quantify the accuracy of predicted price movements. The experimental results show that the Transformer-based models outperform standalone recurrent architectures across all forecasting methods, with the Transformer-LSTM achieving the lowest error values and strong trend-tracking behavior. These findings highlight the effectiveness of hybrid attention-recurrent architectures for modeling the nonlinear and volatile dynamics of Bitcoin markets.
Keywords:
Bitcoin, cryptocurrency forecasting, deep learning, transformer, LSTM, GRU, multi-step forecastingDownloads
References
S. Nakamoto, "Bitcoin: A Peer-to-Peer Electronic Cash System," SSRN Electronic Journal, 2008.
"Global Cryptocurrency Market Cap Charts," CoinGecko, Nov. 2025. https://www.coingecko.com/en/charts.
S. McNally, J. Roche, and S. Caton, "Predicting the Price of Bitcoin Using Machine Learning," in 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing, Cambridge, UK, Mar. 2018, pp. 339–343. DOI: https://doi.org/10.1109/PDP2018.2018.00060
S. Lahmiri and S. Bekiros, "Cryptocurrency Forecasting with Deep Learning Chaotic Neural Networks," Chaos, Solitons & Fractals, vol. 118, pp. 35–40, Jan. 2019. DOI: https://doi.org/10.1016/j.chaos.2018.11.014
Y. Li and W. Dai, "Bitcoin Price Forecasting Method Based on CNN‐LSTM Hybrid Neural Network Model," The Journal of Engineering, vol. 2020, no. 13, pp. 344–347, Jul. 2020. DOI: https://doi.org/10.1049/joe.2019.1203
A. Politis, K. Doka, and N. Koziris, "Ether Price Prediction Using Advanced Deep Learning Models," in 2021 IEEE International Conference on Blockchain and Cryptocurrency, Sydney, Australia, May 2021, pp. 1–3. DOI: https://doi.org/10.1109/ICBC51069.2021.9461061
A. Vaswani et al., "Attention Is All You Need," in 31st Conference on Neural Information Processing Systems, Long Beach, CA, USA, 2017.
H. Wu, J. Xu, J. Wang, and M. Long, "Autoformer: Decomposition Transformers with Auto-correlation for Long-Term Series Forecasting," in Proceedings of the 35th International Conference on Neural Information Processing Systems, Virtual (Online), 2021, pp. 22419–22430.
H. Zhou et al., "Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting," Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 12, pp. 11106–11115, May 2021. DOI: https://doi.org/10.1609/aaai.v35i12.17325
M. A. Izadi and E. Hajizadeh, "Time Series Prediction for Cryptocurrency Markets with Transformer and Parallel Convolutional Neural Networks," Applied Soft Computing, vol. 177, Jun. 2025, Art. no. 113229. DOI: https://doi.org/10.1016/j.asoc.2025.113229
M. A. L. Khaniki and M. Manthouri, "Enhancing Price Prediction in Cryptocurrency Using Transformer Neural Network and Technical Indicators," arXiv, Mar. 06, 2024.
M. Dixon, D. Klabjan, and J. H. Bang, "Classification-Based Financial Markets Prediction Using Deep Neural Networks," Algorithmic Finance, vol. 6, no. 3–4, pp. 67–77, Dec. 2017. DOI: https://doi.org/10.3233/AF-170176
T. Fischer and C. Krauss, "Deep Learning with Long Short-Term Memory Networks for Financial Market Predictions," European Journal of Operational Research, vol. 270, no. 2, pp. 654–669, Oct. 2018. DOI: https://doi.org/10.1016/j.ejor.2017.11.054
J. Sun, Y. Zhou, and J. Lin, "Using Machine Learning for Cryptocurrency Trading," in 2019 IEEE International Conference on Industrial Cyber Physical Systems, Taipei, Taiwan, May 2019, pp. 647–652. DOI: https://doi.org/10.1109/ICPHYS.2019.8780358
G. Attanasio, L. Cagliero, P. Garza, and E. Baralis, "Quantitative Cryptocurrency Trading: Exploring the Use of Machine Learning Techniques," in Proceedings of the 5th Workshop on Data Science for Macro-modeling with Financial and Economic Datasets, Amsterdam, Netherlands, Jun. 2019, pp. 1–6. DOI: https://doi.org/10.1145/3336499.3338003
H. Sebastião and P. Godinho, "Forecasting and Trading Cryptocurrencies with Machine Learning Under Changing Market Conditions," Financial Innovation, vol. 7, no. 1, Jan. 2021, Art. no. 3. DOI: https://doi.org/10.1186/s40854-020-00217-x
T. A. Borges and R. F. Neves, "Ensemble of Machine Learning Algorithms for Cryptocurrency Investment with Different Data Resampling Methods," Applied Soft Computing, vol. 90, May 2020, Art. no. 106187. DOI: https://doi.org/10.1016/j.asoc.2020.106187
M. J. Hamayel and A. Y. Owda, "A Novel Cryptocurrency Price Prediction Model Using GRU, LSTM and Bi-LSTM Machine Learning Algorithms," AI, vol. 2, no. 4, pp. 477–496, Oct. 2021. DOI: https://doi.org/10.3390/ai2040030
S. Goutte, H.-V. Le, F. Liu, and H.-J. Von Mettenheim, "Deep Learning and Technical Analysis in Cryptocurrency Market," Finance Research Letters, vol. 54, Jun. 2023, Art. no. 103809. DOI: https://doi.org/10.1016/j.frl.2023.103809
J. Bouslimi, S. Boubaker, and K. Tissaoui, "Forecasting of Cryptocurrency Price and Financial Stability: Fresh Insights Based on Big Data Analytics and Deep Learning Artificial Intelligence Techniques," Engineering, Technology & Applied Science Research, vol. 14, no. 3, pp. 14162–14169, Jun. 2024. DOI: https://doi.org/10.48084/etasr.7096
C. Y. Kang, C. P. Lee, and K. M. Lim, "Cryptocurrency Price Prediction with Convolutional Neural Network and Stacked Gated Recurrent Unit," Data, vol. 7, no. 11, Oct. 2022, Art. no. 149. DOI: https://doi.org/10.3390/data7110149
I. E. Livieris, N. Kiriakidou, S. Stavroyiannis, and P. Pintelas, "An Advanced CNN-LSTM Model for Cryptocurrency Forecasting," Electronics, vol. 10, no. 3, Jan. 2021, Art. no. 287. DOI: https://doi.org/10.3390/electronics10030287
O. M. Ahmed, L. M. Haji, A. M. Ahmed, and N. M. Salih, "Bitcoin Price Prediction Using the Hybrid Convolutional Recurrent Model Architecture," Engineering, Technology & Applied Science Research, vol. 13, no. 5, pp. 11735–11738, Oct. 2023. DOI: https://doi.org/10.48084/etasr.6223
M. Ortu, N. Uras, C. Conversano, S. Bartolucci, and G. Destefanis, "On Technical Trading and Social Media Indicators for Cryptocurrency Price Classification Through Deep Learning," Expert Systems with Applications, vol. 198, Jul. 2022, Art. no. 116804. DOI: https://doi.org/10.1016/j.eswa.2022.116804
R. Parekh et al., "DL-GuesS: Deep Learning and Sentiment Analysis-Based Cryptocurrency Price Prediction," IEEE Access, vol. 10, pp. 35398–35409, 2022. DOI: https://doi.org/10.1109/ACCESS.2022.3163305
N. Uras and M. Ortu, "Investigation of Blockchain Cryptocurrencies’ Price Movements Through Deep Learning: A Comparative Analysis," in 2021 IEEE International Conference on Software Analysis, Evolution and Reengineering, Honolulu, HI, USA, Mar. 2021, pp. 715–722. DOI: https://doi.org/10.1109/SANER50967.2021.00091
M. Rahmani Cherati, A. Haeri, and S. F. Ghannadpour, "Cryptocurrency Direction Forecasting Using Deep Learning Algorithms," Journal of Statistical Computation and Simulation, vol. 91, no. 12, pp. 2475–2489, Aug. 2021. DOI: https://doi.org/10.1080/00949655.2021.1899179
M. Khalid Salman and A. Abdu Ibrahim, "Price Prediction of Different Cryptocurrencies Using Technical Trade Indicators and Machine Learning," IOP Conference Series: Materials Science and Engineering, vol. 928, no. 3, Nov. 2020, Art. no. 032007. DOI: https://doi.org/10.1088/1757-899X/928/3/032007
P. K. Nagula and C. Alexakis, "A New Hybrid Machine Learning Model for Predicting the Bitcoin (BTC-USD) Price," Journal of Behavioral and Experimental Finance, vol. 36, Dec. 2022, Art. no. 100741. DOI: https://doi.org/10.1016/j.jbef.2022.100741
D.-G. Anghel, "A Reality Check on Trading Rule Performance in the Cryptocurrency Market: Machine Learning vs. Technical Analysis," Finance Research Letters, vol. 39, Mar. 2021, Art. no. 101655. DOI: https://doi.org/10.1016/j.frl.2020.101655
F. Fang et al., "Ascertaining Price Formation in Cryptocurrency Markets with Machine Learning," The European Journal of Finance, vol. 30, no. 1, pp. 78–100, Jan. 2024. DOI: https://doi.org/10.1080/1351847X.2021.1908390
R. Hasanli and M. Dursun, "Integrating High-Dimensional Technical Indicators into Machine Learning Models for Predicting Cryptocurrency Price Movements and Trading Performance: Evidence from Bitcoin, Ethereum, and Ripple," FinTech, vol. 4, no. 4, Dec. 2025, Art. no. 77. DOI: https://doi.org/10.3390/fintech4040077
"BTC-USD," Yahoo Finance, Nov. 2025. https://finance.yahoo.com/quote/BTC-USD/.
S. Hochreiter and J. Schmidhuber, "Long Short-Term Memory," Neural Computation, vol. 9, no. 8, pp. 1735–1780, Nov. 1997. DOI: https://doi.org/10.1162/neco.1997.9.8.1735
K. Cho, B. Van Merrienboer, D. Bahdanau, and Y. Bengio, "On the Properties of Neural Machine Translation: Encoder–Decoder Approaches," in Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation, Doha, Qatar, 2014, pp. 103–111. DOI: https://doi.org/10.3115/v1/W14-4012
M. Schuster and K. K. Paliwal, "Bidirectional Recurrent Neural Networks," IEEE Transactions on Signal Processing, vol. 45, no. 11, pp. 2673–2681, Nov. 1997. DOI: https://doi.org/10.1109/78.650093
C. Varalakshmi, M. Ranka, S. Christina, M. M. Sucharitha, and M. S. A. Basha, "Deploying a Multi-Model Forecasting System for Bitcoin Prices: Bridging Statistical Forecasting and Deep Learning Innovations," in 3rd International Conference on Data Science and Information System, Hassan, India, May 2025, pp. 1–8. DOI: https://doi.org/10.1109/ICDSIS65355.2025.11070535
M. Mudassir, S. Bennbaia, D. Unal, and M. Hammoudeh, "Time-Series Forecasting of Bitcoin Prices Using High-Dimensional Features: A Machine Learning Approach," Neural Computing and Applications, vol. 37, no. 28, pp. 22979–22993, Jul. 2020. DOI: https://doi.org/10.1007/s00521-020-05129-6
V. Derbentsev, N. Datsenko, O. Stepanenko, and V. Bezkorovainyi, "Forecasting Cryptocurrency Prices Time Series Using Machine Learning Approach," SHS Web of Conferences, vol. 65, 2019, Art. no. 02001. DOI: https://doi.org/10.1051/shsconf/20196502001
A. J. Amadeo, J. G. Siento, T. A. Eikwine, Diana, and I. H. Parmonangan, "Temporal Fusion Transformer for Multi Horizon Bitcoin Price Forecasting," in 2023 IEEE 9th Information Technology International Seminar, Batu Malang, Indonesia, Oct. 2023, pp. 1–7. DOI: https://doi.org/10.1109/ITIS59651.2023.10420330
N. Sizykh, S. Dandamaev, and D. Sizykh, "Application of the Method of Multivariate Multi-stage Forecasting Based on the LSTM Deep Learning Model for Bitcoin Price Time Series," in 2023 16th International Conference Management of Large-Scale System Development, Moscow, Russian Federation, Sept. 2023, pp. 1–5. DOI: https://doi.org/10.1109/MLSD58227.2023.10304008
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