Cryptocurrency Price Prediction Using Supervised Machine Learning Algorithms
Abstract As a consequence of rising geo-economic issues, global currency values have declined during the last two years, stock markets have performed poorly, and investors have lost money. Consequently, there is a renewed interest in digital currencies. Cryptocurrency is a fresh kind of asset that has evolved as a result of fintech innovations, and it has provided a major research opportunity. Due to price fluctuation and dynamism, anticipating the price of cryptocurrencies is difficult. There are hundreds of cryptocurrencies in circulation around the world and the demand to use a prediction system for price forecasting has increased manifold. Hence, many developers have proposed machine learning algorithms for price forecasting. Machine learning is fast evolving, with several theoretical advances and applications in a variety of domains. This study proposes the use of three supervised machine learning methods, namely linear regression, support vector machine, and decision tree, to estimate the price of four prominent cryptocurrencies: Bitcoin, Ethereum, Dogecoin, and Bitcoin Cash. The purpose of this study is to compute and compare the precision of all three techniques over all four datasets.
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Matta, M., Lunesu, I., & Marchesi, M. (2015). Bitcoin spread prediction using social and web search media. UMAP workshops, 1-10.
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Shah, D., & Zhang, K. (2014). Bayesian regression and Bitcoin. 52nd Annual Allerton Conference on Communication control and Computing (Allerton), 409-414. https://doi.org/10.1109/ALLERTON.2014.7028484
Shin, M. J., Mohaisen, D., & Kim, J. (2021). Bitcoin price forecasting via ensemble-based LSTM deep learning networks. International Conference on Information Networking (ICOIN), Jeju Island, Republic of Korea, 603-608. https://doi.org/10.1109/ICOIN50884.2021.9333853
Velankar, S., Valecha, S., & Maji, S. (2018). Bitcoin price prediction using machine learning. 20th International Conference on Advanced Communication Technology (ICACT), 144-147. https://doi.org/10.23919/ICACT.2018.8323675
Wei, W., Zhang, Q., & Ling, L. (2021). Bitcoin transaction forecasting with deep network representation learning. IEEE 543 Transactions on Emerging Topics in Computing, 9(3), 1359-1371. https://doi.org/10.1109/TETC.2020.3010464
Yuneline, M. H. (2019). Analysis of cryptocurrency’s characteristics in four perspectives. Journal Of Asian Business And Economic Studies, 26(2), 206-219. https://doi.org/10.1108/jabes-12-2018-0107
Zhao, Y., & Chen, Z., (2021). Forecasting stock price movement: new evidence from a novel hybrid deep learning model. Journal Of Asian Business And Economic Studies, 29(2), 91-104. https://doi.org/10.1108/jabes-05-2021-0061
Andrianto, Y., & Diputra, Y. (2017). The Effect of Cryptocurrency on Investment Portfolio Effectiveness. Journal of Finance and Accounting, 5(6), 229-238. https://doi.org/10.11648/j.jfa.20170506.14
Awotunde, J. B., Ogundokun, R. O., Jimoh, R. G., Misra, S., & Aro, T. O. (2021). Machine Learning Algorithm for Cryptocurrencies Price Prediction. Artificial Intelligence for Cyber Security: Methods, Issues, and Possible Horizons or Opportunities, 972, 421-447. https://doi.org/10.1007/978-3-030-72236-4_17
Bangroo, R., Gupta, U., Sah, R., and Kumar, A. (2022), “Cryptocurrency price prediction using machine learning algorithm,” 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 1-4. Noida, India. https://doi.org/10.1109/ICRITO56286.2022.9964870
Basher, S. A., and Sadorsky, P. (2022). Forecasting Bitcoin price direction with random forests: How important are interest rates, inflation, and market volatility? Machine Learning With Applications, 9, 100355. https://doi.org/10.1016/j.mlwa.2022.100355
Chen, R., & Lazer, M. (2013). Sentiment analysis of Twitter feeds for the prediction of stock market movement. Stanford edu Retrieved, January 25.
Chen, Z., Li, C., & Sun, W. (2020). Bitcoin price prediction using machine learning: An approach to sample dimension engineering. Journal Of Computational And Applied Mathematics, 365, 112395. https://doi.org/10.1016/j.cam.2019.112395
Cherati, M. R., Haeri, A., & Ghannadpour, S. F. (2021). Cryptocurrency direction forecasting using deep learning algorithms. Journal Of Statistical Computation And Simulation, 91(12), 2475-2489. https://doi.org/10.1080/00949655.2021.1899179
Chintalapati, J. B., Pratyusha, P. P. S., Keerthi, V. U., & Jatin, K., S. (2020). Bitcoin price prediction using time series analysis observations marked with a timestamp. Journal of Engineering Sciences, 11(5), 8-14.
Derbentsev, V., Babenko, V., Khrustalev, K., Obruch, H., & Khrustalova, S. (2021). Comparative performance of machine learning ensemble algorithms for forecasting cryptocurrency prices. International Journal of Engineering, 34(1), 140-148. https://doi.org/10.5829/ije.2021.34.01a.16
Dimitriadou, A., & Gregoriou, A. (2023). Predicting Bitcoin prices using machine Learning. Entropy, 25(5), 777. https://doi.org/10.3390/e25050777
Georgoula, I., Pournarakis, D., Bilanakos, C., Sotiropoulos, D., and Giaglis, G. M. (2014). Using Time-Series and Sentiment Analysis to Detect the Determinants of Bitcoin Prices. Social Science Research Network. Available at SSRN 2607167. https://doi.org/10.2139/ssrn.2607167
Go, A., Bhayani, R., & Huang, L. (2009). Twitter sentiment classification using distant supervision. CS224N project report, 1(12). Stanford.
Hosure, N. S., Gaikwad J. V., Shravani M. R., Kulloli N., & Madhusudhan H. S. (2022). Cryptocurrency price prediction using machine learning. International Journal of Advanced Research in Computer and Communication Engineering, 11(6), 645-651.
Hota, H. S., Handa, R., & Shrivas, A. K. (2017). Time series data prediction using sliding window based RBF neural network. International Journal of Computational Intelligence Research, 13(5), 1145-1156.
Ibrahim, A., Kashef, R., & Corrigan, L. (2021). Predicting market movement direction for bitcoin: A comparison of time series modeling methods. Computers & Electrical Engineering, 89, 106905. https://doi.org/10.1016/j.compeleceng.2020.106905
Jang, H. & Lee, J. (2017). An empirical study on modeling and prediction of bitcoin prices with Bayesian neural networks based on blockchain information. IEEE Access, 6, 5427-5437. https://doi.org/10.1109/access.2017.2779181
Jaquart, P., Dann, D., & Weinhardt, C. (2021). Short-term bitcoin market prediction via machine learning. The Journal Of Finance And Data Science, 7, 45-66. https://doi.org/10.1016/j.jfds.2021.03.001
Madan, I., Saluja, S., & Zhao, A. (2015). Automated bitcoin trading via machine learning algorithms. URL: http://cs229.Stanford.edu/proj2014/Isaac%20Madan, 20.
Matta, M., Lunesu, I., & Marchesi, M. (2015). Bitcoin spread prediction using social and web search media. UMAP workshops, 1-10.
McNally, S., Roche, J., and Caton, S. (2018). Predicting the price of Bitcoin using machine learning 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP), 339-343. https://doi.org/10.1109/PDP2018.2018.00060
Shah, D., & Zhang, K. (2014). Bayesian regression and Bitcoin. 52nd Annual Allerton Conference on Communication control and Computing (Allerton), 409-414. https://doi.org/10.1109/ALLERTON.2014.7028484
Shin, M. J., Mohaisen, D., & Kim, J. (2021). Bitcoin price forecasting via ensemble-based LSTM deep learning networks. International Conference on Information Networking (ICOIN), Jeju Island, Republic of Korea, 603-608. https://doi.org/10.1109/ICOIN50884.2021.9333853
Velankar, S., Valecha, S., & Maji, S. (2018). Bitcoin price prediction using machine learning. 20th International Conference on Advanced Communication Technology (ICACT), 144-147. https://doi.org/10.23919/ICACT.2018.8323675
Wei, W., Zhang, Q., & Ling, L. (2021). Bitcoin transaction forecasting with deep network representation learning. IEEE 543 Transactions on Emerging Topics in Computing, 9(3), 1359-1371. https://doi.org/10.1109/TETC.2020.3010464
Yuneline, M. H. (2019). Analysis of cryptocurrency’s characteristics in four perspectives. Journal Of Asian Business And Economic Studies, 26(2), 206-219. https://doi.org/10.1108/jabes-12-2018-0107
Zhao, Y., & Chen, Z., (2021). Forecasting stock price movement: new evidence from a novel hybrid deep learning model. Journal Of Asian Business And Economic Studies, 29(2), 91-104. https://doi.org/10.1108/jabes-05-2021-0061
Chaudhary, D., & Saroj, S. K. (2023). Cryptocurrency Price Prediction Using Supervised Machine Learning Algorithms. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 12(1), e31490. https://doi.org/10.14201/adcaij.31490
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