Comparative Analysis of Bitcoin Price Prediction Models: A Systematic literature review
DOI:
https://doi.org/10.61467/2007.1558.2026.v17i2.1166Keywords:
Bitcoin, cryptocurrencies, price prediction, Machine learning, evaluation metrics, datasetsAbstract
This paper presents a systematic literature review of machine learning models used for predicting Bitcoin prices. We identified and analyzed 26 different machine learning models notable for their effectiveness in predictive tasks. Out of these, 6 models are discussed in detail, focusing on their advantages, disadvantages, and potential hybrid approaches. Additionally, we collected 21 evaluation metrics, identifying 8 as the most relevant for cryptocurrency price prediction. In terms of datasets, we found a reliance on public sources such as Kaggle and Yahoo Finance; however, challenges related to the inconsistency in data availability remain. Lastly, we noted a lack of standardized procedures for comparing models, highlighting the need for the development of systematic methodologies to standardize evaluations in this field.
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References
Aljadani, A. (2022). DLCP2F: A DL-based cryptocurrency price prediction framework. Discover Computing, 1(1), 1–15. https://doi.org/10.1007/s44163-022-00036-2
Aljojo, N., Alshutayri, A., Aldhahri, E., Almandeel, S., & Zainol, A. (2021). A nonlinear autoregressive exogenous (NARX) neural network model for the prediction of timestamp influence on Bitcoin value. IEEE Access, 9, 148611–148624. https://doi.org/10.1109/ACCESS.2021.3124629
Carrizo, D., & Moller, C. (2018). Methodological structures of systematic literature review in software engineering: A systematic mapping study. Ingeniare. Revista Chilena de Ingeniería, 26(Suppl. 1), 45–54. https://doi.org/10.4067/S0718-33052018000500045
Choi, H., Jung, C., Kang, T., Kim, H. J., & Kwak, I. Y. (2022). Explainable time-series prediction using a residual network and gradient-based methods. IEEE Access, 10, 110537–110550. https://doi.org/10.1109/ACCESS.2022.3213926
CoinMarketCap. (s. f.). CoinMarketCap. Recuperado el 5 de diciembre de 2024 de https://coinmarketcap.com/
Das Gupta, S., Kolla, T., Yadav, R., Arora, M., & Pandey, M. (2023). Forecasting cryptocurrency prices using sequential and time series models. En Proceedings of the 4th International Conference on Information Management & Machine Intelligence (ICIMMI ’22) (Article 91, pp. 1–6). ACM. https://doi.org/10.1145/3590837.3590928
Haddaway, N. R., Page, M. J., Pritchard, C. C., & McGuinness, L. A. (2022). PRISMA2020: An R package and Shiny app for producing PRISMA 2020-compliant flow diagrams. Campbell Systematic Reviews, 18(2), e1230. https://doi.org/10.1002/cl2.1230
Iqbal, M., Iqbal, A., Alshammari, A., Ali, I., Maghrabi, L. A., & Usman, N. (2024). Sell or HODL cryptos: Cryptocurrency short-to-long term projection using simultaneous classification-regression deep learning framework. IEEE Access, 12, 118169–118184. https://doi.org/10.1109/ACCESS.2024.3448234
Jay, P., Kalariya, V., Parmar, P., Tanwar, S., Kumar, N., & Alazab, M. (2020). Stochastic neural networks for cryptocurrency price prediction. IEEE Access, 8, 70294–70308. https://doi.org/10.1109/ACCESS.2020.2990659
Jones, J., & Demirel, D. (2022). Long short-term memory for Bitcoin price prediction. En Proceedings of the 6th International Conference on Information System and Data Mining (ICISDM ’22) (pp. 25–30). ACM. https://doi.org/10.1145/3546157.3546162
Kalyani, K., Parvathy, V. S., Abdeljaber, H. A., Murthy, T. S., & Acharya, S. (2023). Effective return rate prediction of blockchain financial products using machine learning. Computers, Materials & Continua, 74(1), 2303–2316. https://doi.org/10.32604/cmc.2023.033162
Kitchenham, B., Brereton, O. P., Budgen, D., Turner, M., Bailey, J., & Linkman, S. (2009). Systematic literature reviews in software engineering. Information and Software Technology, 51(1), 7–15. https://doi.org/10.1016/j.infsof.2008.09.009
Liu, C., Arulappan, A., Naha, R., Mahanti, A., Kamruzzaman, J., & Ra, I.-H. (2024). Large language models and sentiment analysis in financial markets: A review, datasets, and case study. IEEE Access, 12, 134041–134061. https://doi.org/10.1109/ACCESS.2024.3445413
Mahfooz, A., & Phillips, J. L. (2024). Conditional forecasting of Bitcoin prices using exogenous variables. IEEE Access, 12, 44510–44526. https://doi.org/10.1109/ACCESS.2024.3381516
Muminov, A., Sattarov, O., & Na, D. (2024). Enhanced Bitcoin price direction forecasting with DQN. IEEE Access, 12, 29093–29112. https://doi.org/10.1109/ACCESS.2024.3367719
Nayak, S. C., Das, S., Dehuri, S., & Cho, S.-B. (2023). An elitist artificial electric field algorithm based random vector functional link network for cryptocurrency prices forecasting. IEEE Access, 11, 57693–57716. https://doi.org/10.1109/ACCESS.2023.3283571
Oh, I.-S., & Lee, J.-S. (2022). Dense sampling of time series for forecasting. IEEE Access, 10, 75571–75580. https://doi.org/10.1109/ACCESS.2022.3191668
Parekh, R., et al. (2022). DL-GuesS: Deep learning and sentiment analysis-based cryptocurrency price prediction. IEEE Access, 10, 35398–35409. https://doi.org/10.1109/ACCESS.2022.3163305
Patel, N., et al. (2022). Fusion in cryptocurrency price prediction: A decade survey on recent advancements, architecture, and potential future directions. IEEE Access, 10, 34511–34538. https://doi.org/10.1109/ACCESS.2022.3163023
PRISMA. (2020). PRISMA 2020: Preferred reporting items for systematic reviews and meta-analyses. https://www.prisma-statement.org/prisma-2020
Sabry, F., Labda, W., Erbad, A., & Malluhi, Q. (2020). Cryptocurrencies and artificial intelligence: Challenges and opportunities. IEEE Access, 8, 175840–175858. https://doi.org/10.1109/ACCESS.2020.3025211
Singh, S., Pise, A., & Yoon, B. (2024). Prediction of Bitcoin stock price using feature subset optimization. Heliyon, 10(7), e28415. https://doi.org/10.1016/j.heliyon.2024.e28415
Syed, S., Iqbal, A., Mehmood, W., Syed, Z., Khan, M., & Pau, G. (2023). Split-second cryptocurrency forecast using prognostic deep learning algorithms: Data curation by Deephaven. IEEE Access, 11, 128644–128654. https://doi.org/10.1109/ACCESS.2023.3331652
Vilca Zuniga, E. W., Ranieri, C. M., Zhao, L., Ueyama, J., Zhu, Y., & Ji, D. (2023). Maximizing portfolio profitability during a cryptocurrency downtrend: A Bitcoin blockchain transaction-based approach. Procedia Computer Science, 222, 539–548. https://doi.org/10.1016/j.procs.2023.08.192
Zhang, Z., & Sang, Q. (2024). Enhancing Bitcoin log-return prediction through TimesNet and sentiment analysis integration. En Proceedings of the 4th International Conference on Machine Learning and Computer Application (ICMLCA 2023) (pp. 82–91). ACM. https://doi.org/10.1145/3650215.3650232
Zhi, Y., & Chan, T.-H. H. (2024). Clustering social media data for Bitcoin price prediction with transformer model. En Proceedings of the 6th International Conference on Machine Learning and Natural Language Processing (MLNLP ’23) (pp. 135–140). ACM. https://doi.org/10.1145/3639479.3639508
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