Harnessing Machine Learning for Crypto-Currency Price Prediction: A Review


Zeravan Arif Ali(1*), Adnan M. Abdulazeez(2)

(1) IT Department, Duhok Technical College, Duhok Polytechnic University, Duhok, Iraq
(2) IT Department, Duhok Technical College, Duhok Polytechnic University, Duhok, Iraq
(*) Corresponding Author

Abstract


Despite their recent inception, cryptocurrencies have become globally recognized for their dispersal, diversity, and high market capitalization. This volatility developed into a challenge for investors looking to predict price movements. Thus, it has become an attractive investment opportunity. To increase prediction accuracy, researchers integrate machine learning algorithms with technical indicators. In this review, a systematic comparison has been employed to identify efficient algorithms, and researchers have employed statistical measures to make short- and long-term forecasts of decentralized money prices. Moreover, the paper highlights the results of researchers based on machine learning and deep learning methodologies on multiple types of cryptocurrencies like Bitcoin, Ethereum, Monero, etc. Lastly, the work emphasizes the limitations, gaps, and challenges facing researchers to take advantage of existing literature for future works.


Keywords


Cryptocurrencies, Deep Learning, Machine Learning, Technical Indicators

Full Text:

PDF

References


S. Ranjan, P. Kayal, and M. Saraf, “Bitcoin Price Prediction: A Machine Learning Sample Dimension Approach,” Comput. Econ., vol. 61, no. 4, pp. 1617–1636, 2023, doi: 10.1007/s10614-022-10262-6.

V. D’Amato, S. Levantesi, and G. Piscopo, Deep learning in predicting cryptocurrency volatility, vol. 596. Physica A: Statistical Mechanics and Its Applications, 596, 127158, 2022. doi: 10.1016/j.physa.2022.127158.

T. Choithani, A. Chowdhury, S. Patel, P. Patel, D. Patel, and M. Shah, “A Comprehensive Study of Artificial Intelligence and Cybersecurity on Bitcoin, Crypto Currency and Banking System,” Ann. Data Sci., vol. 11, no. 1, pp. 103–135, 2024, doi: 10.1007/s40745-022-00433-5.

Z. Chen, C. Li, and W. Sun, “Bitcoin price prediction using machine learning: An approach to sample dimension engineering,” J. Comput. Appl. Math., vol. 365, p. 112395, 2020, doi: 10.1016/j.cam.2019.112395.

T. Zoumpekas, E. Houstis, and M. Vavalis, “ETH analysis and predictions utilizing deep learning,” Expert Syst. Appl., vol. 162, p. 113866, 2020, doi: 10.1016/j.eswa.2020.113866.

M. M. Patel, S. Tanwar, R. Gupta, and N. Kumar, “A Deep Learning-based Cryptocurrency Price Prediction Scheme for Financial Institutions,” J. Inf. Secur. Appl., vol. 55, p. 102583, 2020, doi: 10.1016/j.jisa.2020.102583.

N. C. Sattaru, D. Umrao, K. K. Ramachandran, K. K. Karthick, M. Tiwari, and M. V. S. Kumar, “Machine Learning as a Predictive Technology And Its Impact on Digital Pricing and Cryptocurrency Markets,” 2022. doi: 10.1109/ICACITE53722.2022.9823734.

H. Kavitha, U. K. Sinha, and S. S. Jain, “Performance Evaluation of Machine Learning Algorithms for Bitcoin Price Prediction,” 2020. doi: 10.1109/ICISC47916.2020.9171147.

B. J. I. Mahesh, “Machine learning algorithms-a review.,” Int. J. Sci. Res., vol. 9, no. 1, pp. 381–386, 2020.

L. Yang and A. Shami, “On hyperparameter optimization of machine learning algorithms: Theory and practice,” Neurocomputing, vol. 415, pp. 295–316, 2020, doi: 10.1016/j.neucom.2020.07.061.

Q. Xu and J. Yin, “Application of Random Forest Algorithm in Physical Education,” Sci. Program., vol. 2021, 2021, doi: 10.1155/2021/1996904.

P. Jaquart, D. Dann, and C. Weinhardt, “Short-term bitcoin market prediction via machine learning,” J. Financ. Data Sci., vol. 7, pp. 45–66, 2021, doi: 10.1016/j.jfds.2021.03.001.

P. Jaquart, S. Köpke, and C. Weinhardt, “Machine learning for cryptocurrency market prediction and trading,” J. Financ. Data Sci., vol. 8, pp. 331–352, 2022, doi: 10.1016/j.jfds.2022.12.001.

Y. Liu, Z. Li, R. Nekhili, and J. Sultan, “Forecasting cryptocurrency returns with machine learning,” Res. Int. Bus. Financ., vol. 64, p. 101905, 2023, doi: 10.1016/j.ribaf.2023.101905.

H. M. Kim, G. W. Bock, and G. Lee, “Predicting Ethereum prices with machine learning based on Blockchain information,” Expert Syst. Appl., vol. 184, p. 115480, 2021, doi: 10.1016/j.eswa.2021.115480.

T. A. Borges and R. F. J. A. S. C. Neves, “Ensemble of machine learning algorithms for cryptocurrency investment with different data resampling methods,” vol. 90, p. 106187, 2020.

A. A. Oyedele, A. O. Ajayi, L. O. Oyedele, S. A. Bello, and K. O. Jimoh, “Performance evaluation of deep learning and boosted trees for cryptocurrency closing price prediction,” Expert Syst. Appl., vol. 213, p. 119233, 2023, doi: 10.1016/j.eswa.2022.119233.

Y. Fu, A. R. J. Downey, L. Yuan, T. Zhang, A. Pratt, and Y. Balogun, “Machine learning algorithms for defect detection in metal laser-based additive manufacturing: A review,” J. Manuf. Process., vol. 75, pp. 693–710, 2022, doi: 10.1016/j.jmapro.2021.12.061.

A. Ajayi et al., “Optimised Big Data analytics for health and safety hazards prediction in power infrastructure operations,” Saf. Sci., vol. 125, p. 104656, 2020, doi: 10.1016/j.ssci.2020.104656.

A. Dutta, S. Kumar, and M. Basu, “A Gated Recurrent Unit Approach to Bitcoin Price Prediction,” J. Risk Financ. Manag., vol. 13, no. 2, p. 23, 2020, doi: 10.3390/jrfm13020023.

S. Lahmiri and S. Bekiros, “Intelligent forecasting with machine learning trading systems in chaotic intraday Bitcoin market,” Chaos, Solitons and Fractals, vol. 133, p. 109641, 2020, doi: 10.1016/j.chaos.2020.109641.

J. Chevallier, B. Zhu, and L. Zhang, “Forecasting Inflection Points: Hybrid Methods with Multiscale Machine Learning Algorithms,” Comput. Econ., vol. 57, no. 2, pp. 537–575, 2021, doi: 10.1007/s10614-019-09966-z.

K. Shah, H. Patel, D. Sanghvi, and M. Shah, “A Comparative Analysis of Logistic Regression, Random Forest and KNN Models for the Text Classification,” Augment. Hum. Res., vol. 5, no. 1, p. 12, 2020, doi: 10.1007/s41133-020-00032-0.

M. Poongodi, T. N. Nguyen, M. Hamdi, and K. Cengiz, “Global cryptocurrency trend prediction using social media,” Inf. Process. Manag., vol. 58, no. 6, p. 102708, 2021, doi: 10.1016/j.ipm.2021.102708.

O. Kraaijeveld and J. De Smedt, “The predictive power of public Twitter sentiment for forecasting cryptocurrency prices,” J. Int. Financ. Mark. Institutions Money, vol. 65, p. 101188, 2020, doi: 10.1016/j.intfin.2020.101188.

V. Derbentsev, V. Babenko, K. Khrustalev, H. Obruch, and S. Khrustalova, “Comparative performance of machine learning ensemble algorithms for forecasting cryptocurrency prices,” Int. J. Eng. Trans. A Basics, vol. 34, no. 1, pp. 140–148, 2021, doi: 10.5829/IJE.2021.34.01A.16.

E. Akyildirim, O. Cepni, S. Corbet, and G. S. Uddin, “Forecasting mid-price movement of Bitcoin futures using machine learning,” Ann. Oper. Res., vol. 330, no. 1–2, pp. 553–584, 2023, doi: 10.1007/s10479-021-04205-x.

C. K. Shruthi, S. Anbarasu, and J. Sabarish, “Crytocurrency price prediction using machine learning,” World J. Adv. Eng. Technol. Sci., vol. 8, no. 1, pp. 251–257, 2023.

H. K. Andi, “An Accurate Bitcoin Price Prediction using logistic regression with LSTM Machine Learning model,” J. Soft Comput. Paradig., vol. 3, no. 3, pp. 205–217, 2021, doi: 10.36548/jscp.2021.3.006.

R. Chowdhury, M. A. Rahman, M. S. Rahman, and M. R. C. Mahdy, “An approach to predict and forecast the price of constituents and index of cryptocurrency using machine learning,” Phys. A Stat. Mech. its Appl., vol. 551, p. 124569, 2020, doi: 10.1016/j.physa.2020.124569.

G. Sahi, T. Saluja, and P. Nargotra, “Predicting Cryptocurrency Price Using Machine Learning,” Eur. Econ. Lett. ISSN http//eelet.org.uk Predict., vol. 13, no. 1, pp. 2323–5233, 2023.

M. Mudassir, S. Bennbaia, D. Unal, and M. Hammoudeh, “Time-series forecasting of Bitcoin prices using high-dimensional features: a machine learning approach,” Neural Comput. Appl., pp. 1–15, 2020, doi: 10.1007/s00521-020-05129-6.

B. N. Nikitha, A. S. Maheswari, D. Shameena, B. Poojasri, H. Kauser, and G. S. Rao, “Crypto Currency Price Prediction with Machine Learning Using Python,” Int. J. Innov. Res. Comput. Sci. Technol., vol. 10, no. 3, pp. 398–402, 2022, doi: 10.55524/ijircst.2022.10.3.63.

R. S. Gadey, N. Thakur, N. Charan, and R. Obulakonda Reddy, “Price Prediction of Bitcoin Using Machine Learning,” Int. J. Eng. Appl. Sci. Technol., vol. 5, no. 1, pp. 502–506, 2020, doi: 10.33564/ijeast.2020.v05i01.089.

S. Mounika, “Crypto-Currency Price Prediction using CNN and LSTM Models,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 9, no. 3, pp. 107–114, 2021, doi: 10.22214/ijraset.2021.33191.

L. Vaddi, “Predicting Crypto Currency Prices Using Machine Learning and Deep Learning Techniques,” Int. J. Adv. Trends Comput. Sci. Eng., vol. 9, no. 4, pp. 6603–6608, 2020, doi: 10.30534/ijatcse/2020/351942020.

S. Marne, D. Correia, S. Churi, and J. Gomes, “Predicting Price of Cryptocurrency-A Deep Learning Approach,” 2021. [Online]. Available: www.ijert.org

M. Iqbal, M. Iqbal, F. Jaskani, K. Iqbal, and A. Hassan, “Time-Series Prediction of Cryptocurrency Market using Machine Learning Techniques,” EAI Endorsed Trans. Creat. Technol., vol. 8, no. 28, p. 170286, 2021, doi: 10.4108/eai.7-7-2021.170286.

A. Nesarani, R. Ramar, and S. Pandian, “An efficient approach for rice prediction from authenticated Block chain node using machine learning technique,” Environ. Technol. Innov., vol. 20, p. 101064, 2020, doi: 10.1016/j.eti.2020.101064.

S. Erfanian, Y. Zhou, A. Razzaq, A. Abbas, A. A. Safeer, and T. Li, “Predicting Bitcoin (BTC) Price in the Context of Economic Theories: A Machine Learning Approach,” Entropy, vol. 24, no. 10, p. 1487, 2022, doi: 10.3390/e24101487.

A. Dimitriadou and A. Gregoriou, “Predicting Bitcoin Prices Using Machine Learning,” Entropy, vol. 25, no. 5, p. 777, 2023, doi: 10.3390/e25050777.

G. Kim, D. H. Shin, J. G. Choi, and S. Lim, “A Deep Learning-Based Cryptocurrency Price Prediction Model That Uses On-Chain Data,” IEEE Access, vol. 10, pp. 56232–56248, 2022, doi: 10.1109/ACCESS.2022.3177888.

M. A. Ammer and T. H. H. Aldhyani, “Deep Learning Algorithm to Predict Cryptocurrency Fluctuation Prices: Increasing Investment Awareness,” Electron., vol. 11, no. 15, p. 2349, 2022, doi: 10.3390/electronics11152349.

Z. Shahbazi and Y. C. Byun, “Knowledge Discovery on Cryptocurrency Exchange Rate Prediction Using Machine Learning Pipelines,” Sensors, vol. 22, no. 5, p. 1740, 2022, doi: 10.3390/s22051740.

S. A. Basher and P. Sadorsky, “Forecasting Bitcoin Price Direction With Random Forests: How Important Are Interest Rates, Inflation, and Market Volatility?,” SSRN Electron. J., vol. 9, p. 100355, 2022, doi: 10.2139/ssrn.4128509.

A. Aljadani, “DLCP2F: a DL-based cryptocurrency price prediction framework,” Discov. Artif. Intell., vol. 2, no. 1, p. 20, 2022, doi: 10.1007/s44163-022-00036-2.

S. Lahmiri and S. Bekiros, “Deep Learning Forecasting in Cryptocurrency High-Frequency Trading,” Cognit. Comput., vol. 13, no. 2, pp. 485–487, 2021, doi: 10.1007/s12559-021-09841-w.

M. Nair, M. I. Marie, and L. A. Abd-Elmegid, “Prediction of Cryptocurrency Price using Time Series Data and Deep Learning Algorithms,” Int. J. Adv. Comput. Sci. Appl., vol. 14, no. 8, pp. 338–347, 2023, doi: 10.14569/IJACSA.2023.0140837.

P. Jay, V. Kalariya, P. Parmar, S. Tanwar, N. Kumar, and M. Alazab, “Stochastic neural networks for cryptocurrency price prediction,” IEEE Access, vol. 8, pp. 82804–82818, 2020, doi: 10.1109/ACCESS.2020.2990659.

E. Akyildirim, A. Goncu, and A. Sensoy, “Prediction of cryptocurrency returns using machine learning,” Ann. Oper. Res., vol. 297, no. 1–2, pp. 3–36, 2021, doi: 10.1007/s10479-020-03575-y.

R. Albariqi and E. Winarko, “Prediction of bitcoin price change using neural networks. Paper presented at the 2020 international conference on smart technology and applications (ICoSTA),” 2020.

M. Rafi, Q. A. K. Mirza, M. I. Sohail, M. Aliasghar, A. Aziz, and S. Hameed, “Enhancing Cryptocurrency Price Forecasting Accuracy: A Feature Selection and Weighting Approach With Bi-Directional LSTM and Trend-Preserving Model Bias Correction,” 2023. doi: 10.1109/ACCESS.2023.3287888.




DOI: https://doi.org/10.15575/kubik.v9i1.33423

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Zeravan Ali

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.


Journal KUBIK: Jurnal Publikasi Ilmiah Matematika has indexed by:

SINTA DOAJ Dimensions Google Scholar Garuda Moraref DOI Crossref

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.