Leveraging Machine Learning to Forecast Trends in Cryptocurrency Exchange Markets
DOI:
https://doi.org/10.70619/vol5iss3pp11-19Keywords:
Cryptocurrency Volatility, Machine Learning Algorithms, Price Prediction, Random Forest, Decision-Making FrameworkAbstract
This research investigates the application of machine learning (ML) algorithms to enhance the predictability of movements in the cryptocurrency market, specifically examining changes in Bitcoin prices. Four ML models, Linear Regression (LR), Random Forest (RF), Gradient Boosting Machines (GBM), and Long Short-Term Memory (LSTM), were analyzed using historical data. The performance of these models was evaluated based on their accuracy, precision, and recall. The findings indicated that Random Forest surpassed the others with nearly perfect results in accuracy (0.99), precision (0.99), and recall (0.99). GBM demonstrated strong recall (0.99) but had lower accuracy (0.83) and precision (0.75), while Linear Regression showed commendable performance with accuracy (0.93) and precision (0.97). LSTM yielded the least favorable results. The study concludes that Random Forest is the most dependable model for predicting cryptocurrency prices, providing useful insights for traders and researchers navigating this highly volatile market.
References
Bouteska, A., Mohammad, A. Z., Petr, H., & Kunpeng, Y. (2024). Cryptocurrency price forecasting – A comparative analysis of ensemble learning and deep learning methods. International Review of Financial Analysis. doi:https://doi.org/10.1016/j.irfa.2023.103055
Flare Team. (2024, May 06). Consensus learning: harnessing blockchain for better AI - Flare. Retrieved from Flare Network: https://flare.network/consensus-learning-harnessing-blockchain-for-better-ai/
Hussein, Z., May, S. A., & Sahar , E.-R. A. (2023). Evolution of blockchain consensus algorithms: a review on the latest milestones of blockchain consensus algorithms. Cybersecurity, 127-138.
Katsiampa, P. (2019). Volatility Estimation for Bitcoin: A Comparison of GARCH Models. Economics Letters, 17-20.
Minghui , X., Yong , G., Liu, C., Hu, Q., Yu, D., Xiong, Z., . . . Xiuzhen, C. (2024). Exploring Blockchain Technology through a Modular Lens: A Survey. ACM computing surveys, 1-9.
Renduchintala, T., Alfauri, H., Yang, Z., Pietro, R., & Jain, R. (2022). A Survey of Blockchain Applications in the FinTech Sector. Journal of Open Innovation: Technology, Market and Complexity, 65-71.
Sanghami, V., John, L., & Qin, H. (2023). Machine-Learning-Enhanced Blockchain Consensus With Transaction Prioritization for Smart Cities. IEEE Internet of Things Journal, 6661 - 6672.
Tingting, H., & Kui, G. (2020). Review of Blockchain Consensus Algorithms. Scientific Journal of Intelligent Systems Research, 1-5.
Wu, Y., Zehua, W., Yuxiang, M., & Leung, V. C. (2021). Deep reinforcement learning for blockchain in industrial IoT: A survey. Computer Networks, 1-6.
Yazeed, A., Mohammad, Q., Orieb, A., & Almaiah, M. (2023). Using Blockchain in the Context of the Automotive Industry: A Survey. 2023 International Conference on Information Technology (ICIT) (pp. 123-128). Amman, Jordan: Al Zaytoonah University of Jordan.
Zhang, L., Chen, Y., & Zhang, W. (2021). A Survey on Machine Learning in Cryptocurrency Trading. IEEE Access, 123456-123478.
Zhang, X., Zhang, Y., & Wang, S. (2020). A hybrid model for financial time series forecasting based on ensemble empirical mode decomposition and random forest. Applied Soft Computing, 105905.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Fils Munyawera, Enan N. Muhire
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
