The Ethereum market is famous for its unpredictability and the possibility of significant profits, presenting both opportunities and obstacles for investors and stakeholders. Given the dynamic nature of this ecosystem, accurate prediction of Ethereum’s price movements is essential for informed decision-making. In this project, we propose an innovative approach that leverages ensemble learning techniques, specifically extra trees and random forest regressors, for Ethereum price prediction. By harnessing a diverse dataset encompassing historical Ethereum price data and a comprehensive array of market indicators such as trading volume, sentiment analysis, and technical metrics, we develop and evaluate our models through a rigorous methodology. Our study highlights the effectiveness of our approach in forecasting Ethereum prices with notable accuracy. Through extensive experimentation, we highlight the superior performance of ensemble learning techniques in capturing the intricate patterns inherent in Ethereum's price dynamics. Additionally, we conduct feature importance analysis to uncover the underlying factors driving Ethereum price movements, thereby providing valuable insights for investors and analysts seeking to navigate this complex market. The findings of our research make significant contributions to the expanding field of cryptocurrency price prediction, particularly within the context of Ethereum. Moreover, our project establishes a sturdy groundwork for continued investigation and enhancement of machine learning techniques in predicting cryptocurrency trends. This work aims to empower stakeholders with actionable intelligence, enabling them to make well-informed decisions amidst the nuances of the Ethereum market and the broader cryptocurrency landscape.

Keywords:Ethereum price prediction, cryptocurrency price prediction, learning techniques, machine learning algorithms

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Kumar VT, Santhi S, Shanthi KG, Gokila M. Cryptocurrency price prediction using LSTM and recurrent neural networks. International Conference on Applied Artificial Intelligence and Computing (ICAAIC). Salem, India. 2023, May 04-06. 1–5. IEEE. doi:10.1109/ICAAIC56838.2023.10141048.
2. Jagannath N, Barbulescu T, Sallam KM, Elgendi I, Mcgrath B, Jamalipour A, Abdel-Basset M, Munasinghe K. An on-chain analysis-based approach to predict Ethereum prices. IEEE Access. 2021;9:167972–167989. doi:10.1109/ACCESS.2021.3135620.
3. Nakamoto S. (2021). Bitcoin: a peer-to-peer electronic cash system [Online]. Klausnordby.com. Available from: https://www.klausnordby.com/bitcoin/Bitcoin_Whitepaper_Document_HD.pdf.
4. Khedr AM, Arif I, Raj PPV, El-Bannany M, Alhashmi and SM, Sreedharan M. Cryptocurrency price prediction using traditional statistical and machine-learning techniques: A survey. Intell Syst Account Finance Manag. 2021;28(1):3–34. doi:10.1002/isaf.1488.
5. Jagannath N, Barbulescu T, Sallam KM, Elgendi I, Okon AA, Mcgrath B, Jamalipour A, Munasinghe K. A self- adaptive deep learning-based algorithm for predictive analysis of bitcoin price. IEEE Access. 2021;9:34054–34066. doi:10.1109/ACCESS.2021.3061002.
6. Khedr AM, Arif I, Raj PPV, El-Bannany M, Alhashmi SM, Sreedharan M. Cryptocurrency price prediction using traditional statistical and machine‐learning techniques: A survey. Intell Syst Account Finance Manag. 2021;8(1):3–34. doi:10.1002/isaf.1488.
7. Biswas S, Pawar M, Badole S, Galande N, Rathod S. Cryptocurrency price prediction using neural networks and deep learning. 7th International Conference on Advanced Computing and Communication Systems (ICACCS). Coimbatore, India. 2021, March 19-20. 408–413. IEEE. doi:10.1109/ICACCS51430.2021.9441872.
8. Politis A, Doka K, Koziris N. Ether price prediction using advanced deep learning models. International Conference on Blockchain and Cryptocurrency (ICBC). Sydney, Australia. 2021, May 03-06. 1–3. IEEE. doi:10.1109/ICBC51069.2021.9461061.
9. Jay P, Kalariya V, Parmar P, Tanwar S, Kumar N, Alazab M. Stochastic neural networks for cryptocurrency price prediction. IEEE Access. 2020;8:82804–82818. doi:10.1109/ACCESS.2020.2990659.
10. Kalodner H, Möser M, Lee K, Goldfeder S, Plattner M, Chator A, Narayanan A. BlockSci: Design and applications of a blockchain analysis platform. 29th USENIX Security Symposium (USENIX Security 20). Berkeley, California: USENIX Association; 2020. 2721–2738.
11. Xiang X, Wang M, Fan W. A permissioned blockchain-based identity management and user authentication scheme for E-health systems. IEEE Access. 2020;8:171771–171783. doi:10.1109/ACCESS.2020.3022429.
12. Bartoletti M, Carta S, Cimoli T, Saia R. Dissecting ponzi schemes on Ethereum: identification analysis and impact. Fut Gener Comput Syst. 2020;102:259–279. doi:10.1016/j.future.2019.08.014.
13. Chen Z, Li C, Sun W. Bitcoin price prediction using machine learning: An approach to sample dimension engineering. J Comput Appl Maths. 2020;365:112395. doi:10.1016/j.cam.2019.112395.
14. Bhargavi MS, Katti SM, Shilpa M, Kulkarni VP, Prasad S. Transactional data analytics for inferring behavioural traits in Ethereum blockchain network. 16th International Conference on Intelligent Computer Communication and Processing (ICCP). Cluj-Napoca, Romania. 2020, Sept 03-05. 485–490. IEEE. doi:10.1109/ICCP51029.2020.9266176.
15. Poongodi M, Sharma A, Vijayakumar V, Bhardwaj V, Sharma AP, Iqbal R, Kumar R. Prediction of the price of Ethereum blockchain cryptocurrency in an industrial finance system. Comput Elect Eng. 2020;81:106527. doi:10.1016/j.compeleceng.2019.106527.
16. Livieris IE, Pintelas E, Stavroyiannis S, Pintelas P. Ensemble deep learning models for forecasting cryptocurrency time-series. Algorithms. 2020;13(5):121. doi:10.3390/a13050121.
17. Zheng P, Zheng Z, Wu J, Dai HN. XBlock-ETH: Extracting and exploring blockchain data from Ethereum. IEEE Open J Comput Soc. 2020;1:95–106. doi:10.1109/OJCS.2020.2990458.
18. Chen W, Zheng Z, Ngai ECH, Zheng P, Zhou Y. Exploiting blockchain data to detect smart ponzi schemes on Ethereum. IEEE Access. 2019;7:37575–37586. doi:10.1109/ACCESS.2019.2905769.
19. Brauneis A, Mestel R. Price discovery of cryptocurrency: Bitcoin and beyond. Econ Lett. 2018;165:58–61. doi:10.1016/j.econlet.2018.02.001.
20. McNally S, Roche J, Caton S. Predicting the price of bitcoin using machine learning. 26th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP). Cambridge, UK. 2018, Mar 21-23. 339–343. IEEE. doi:10.1109/PDP2018.2018.00060.
21. O’Kane E. Detecting patterns in the Ethereum transactional data using unsupervised learning [master’s thesis]. Dublin, Ireland: University of Dublin, Trinity College; 2018. 1–86.
22. Jang H, Lee J. An empirical study on modelling and prediction of bitcoin prices with Bayesian neural networks based on blockchain information. IEEE Access. 2017;6:5427–5437. doi:10.1109/ACCESS.2017.2779181.
23. Rouhani S, Deters R. Performance analysis of Ethereum transactions in private blockchain. 8th IEEE International Conference on Software Engineering and Service Science (ICSESS). Beijing, China. 2017, Nov 24-26. 70–74. IEEE. doi:10.1109/ICSESS.2017.8342866.
24. Chan W, Olmsted A. Ethereum transaction graph analysis. 12^th International Conference for Internet Technology and Secured Transactions (ICITST). Cambridge, UK. 2017, Dec 11-14. 498–500. IEEE. doi:10.23919/ICITST.2017.8356459.
25. Georgoula I, Pournarakis D, Bilanakos C, Sotiropoulos D, Giaglis GM. Using time-series and sentiment analysis to detect the determinants of bitcoin prices. Mediterranean Conference on Information Systems (MCIS). Samos, Greece. 2015. 1–15.
26. Matta M, Lunesu I, Marchesi M. Bitcoin spread prediction using social and web search media. UMAP Workshops. 2015;1–15.
27. Wood G. Ethereum: A secure decentralised generalised transaction ledger. Ethereum Project Yellow Paper. 2014;1–41.
28. Gu B, Konana P, Liu A, Rajagopalan B, Ghosh J. Identifying information in stock message boards and its implications for stock market efficiency. Workshop on Information Systems and Economics. Los Angeles, CA; 2006.
29. Saad M, Choi J, Nyang D, Kim J, Mohaisen A. Toward characterizing blockchain-based cryptocurrencies for highly accurate predictions. IEEE Syst J. 2020;14(1):321–332. doi:10.1109/JSYST.2019.2927707.
30. Xia P, Wang H, Zhang B, Ji R, Gao B, Wu, Luo L, Xu G. Characterizing cryptocurrency exchange scams. Comput Sec. 2020;98:101993. doi:10.1016/j.cose.2020.101993.

The Ethereum market is famous for its unpredictability and the possibility of significant profits, presenting both opportunities and obstacles for investors and stakeholders. Given the dynamic nature of this ecosystem, accurate prediction of Ethereum’s price movements is essential for informed decision-making. In this project, we propose an innovative approach that leverages ensemble learning techniques, specifically extra trees and random forest regressors, for Ethereum price prediction. By harnessing a diverse dataset encompassing historical Ethereum price data and a comprehensive array of market indicators such as trading volume, sentiment analysis, and technical metrics, we develop and evaluate our models through a rigorous methodology. Our study highlights the effectiveness of our approach in forecasting Ethereum prices with notable accuracy. Through extensive experimentation, we highlight the superior performance of ensemble learning techniques in capturing the intricate patterns inherent in Ethereum's price dynamics. Additionally, we conduct feature importance analysis to uncover the underlying factors driving Ethereum price movements, thereby providing valuable insights for investors and analysts seeking to navigate this complex market. The findings of our research make significant contributions to the expanding field of cryptocurrency price prediction, particularly within the context of Ethereum. Moreover, our project establishes a sturdy groundwork for continued investigation and enhancement of machine learning techniques in predicting cryptocurrency trends. This work aims to empower stakeholders with actionable intelligence, enabling them to make well-informed decisions amidst the nuances of the Ethereum market and the broader cryptocurrency landscape.

Keywords:Ethereum price prediction, cryptocurrency price prediction, learning techniques, machine learning algorithms

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Kumar VT, Santhi S, Shanthi KG, Gokila M. Cryptocurrency price prediction using LSTM and recurrent neural networks. International Conference on Applied Artificial Intelligence and Computing (ICAAIC). Salem, India. 2023, May 04-06. 1–5. IEEE. doi:10.1109/ICAAIC56838.2023.10141048.
2. Jagannath N, Barbulescu T, Sallam KM, Elgendi I, Mcgrath B, Jamalipour A, Abdel-Basset M, Munasinghe K. An on-chain analysis-based approach to predict Ethereum prices. IEEE Access. 2021;9:167972–167989. doi:10.1109/ACCESS.2021.3135620.
3. Nakamoto S. (2021). Bitcoin: a peer-to-peer electronic cash system [Online]. Klausnordby.com. Available from: https://www.klausnordby.com/bitcoin/Bitcoin_Whitepaper_Document_HD.pdf.
4. Khedr AM, Arif I, Raj PPV, El-Bannany M, Alhashmi and SM, Sreedharan M. Cryptocurrency price prediction using traditional statistical and machine-learning techniques: A survey. Intell Syst Account Finance Manag. 2021;28(1):3–34. doi:10.1002/isaf.1488.
5. Jagannath N, Barbulescu T, Sallam KM, Elgendi I, Okon AA, Mcgrath B, Jamalipour A, Munasinghe K. A self- adaptive deep learning-based algorithm for predictive analysis of bitcoin price. IEEE Access. 2021;9:34054–34066. doi:10.1109/ACCESS.2021.3061002.
6. Khedr AM, Arif I, Raj PPV, El-Bannany M, Alhashmi SM, Sreedharan M. Cryptocurrency price prediction using traditional statistical and machine‐learning techniques: A survey. Intell Syst Account Finance Manag. 2021;8(1):3–34. doi:10.1002/isaf.1488.
7. Biswas S, Pawar M, Badole S, Galande N, Rathod S. Cryptocurrency price prediction using neural networks and deep learning. 7th International Conference on Advanced Computing and Communication Systems (ICACCS). Coimbatore, India. 2021, March 19-20. 408–413. IEEE. doi:10.1109/ICACCS51430.2021.9441872.
8. Politis A, Doka K, Koziris N. Ether price prediction using advanced deep learning models. International Conference on Blockchain and Cryptocurrency (ICBC). Sydney, Australia. 2021, May 03-06. 1–3. IEEE. doi:10.1109/ICBC51069.2021.9461061.
9. Jay P, Kalariya V, Parmar P, Tanwar S, Kumar N, Alazab M. Stochastic neural networks for cryptocurrency price prediction. IEEE Access. 2020;8:82804–82818. doi:10.1109/ACCESS.2020.2990659.
10. Kalodner H, Möser M, Lee K, Goldfeder S, Plattner M, Chator A, Narayanan A. BlockSci: Design and applications of a blockchain analysis platform. 29th USENIX Security Symposium (USENIX Security 20). Berkeley, California: USENIX Association; 2020. 2721–2738.
11. Xiang X, Wang M, Fan W. A permissioned blockchain-based identity management and user authentication scheme for E-health systems. IEEE Access. 2020;8:171771–171783. doi:10.1109/ACCESS.2020.3022429.
12. Bartoletti M, Carta S, Cimoli T, Saia R. Dissecting ponzi schemes on Ethereum: identification analysis and impact. Fut Gener Comput Syst. 2020;102:259–279. doi:10.1016/j.future.2019.08.014.
13. Chen Z, Li C, Sun W. Bitcoin price prediction using machine learning: An approach to sample dimension engineering. J Comput Appl Maths. 2020;365:112395. doi:10.1016/j.cam.2019.112395.
14. Bhargavi MS, Katti SM, Shilpa M, Kulkarni VP, Prasad S. Transactional data analytics for inferring behavioural traits in Ethereum blockchain network. 16th International Conference on Intelligent Computer Communication and Processing (ICCP). Cluj-Napoca, Romania. 2020, Sept 03-05. 485–490. IEEE. doi:10.1109/ICCP51029.2020.9266176.
15. Poongodi M, Sharma A, Vijayakumar V, Bhardwaj V, Sharma AP, Iqbal R, Kumar R. Prediction of the price of Ethereum blockchain cryptocurrency in an industrial finance system. Comput Elect Eng. 2020;81:106527. doi:10.1016/j.compeleceng.2019.106527.
16. Livieris IE, Pintelas E, Stavroyiannis S, Pintelas P. Ensemble deep learning models for forecasting cryptocurrency time-series. Algorithms. 2020;13(5):121. doi:10.3390/a13050121.
17. Zheng P, Zheng Z, Wu J, Dai HN. XBlock-ETH: Extracting and exploring blockchain data from Ethereum. IEEE Open J Comput Soc. 2020;1:95–106. doi:10.1109/OJCS.2020.2990458.
18. Chen W, Zheng Z, Ngai ECH, Zheng P, Zhou Y. Exploiting blockchain data to detect smart ponzi schemes on Ethereum. IEEE Access. 2019;7:37575–37586. doi:10.1109/ACCESS.2019.2905769.
19. Brauneis A, Mestel R. Price discovery of cryptocurrency: Bitcoin and beyond. Econ Lett. 2018;165:58–61. doi:10.1016/j.econlet.2018.02.001.
20. McNally S, Roche J, Caton S. Predicting the price of bitcoin using machine learning. 26th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP). Cambridge, UK. 2018, Mar 21-23. 339–343. IEEE. doi:10.1109/PDP2018.2018.00060.
21. O’Kane E. Detecting patterns in the Ethereum transactional data using unsupervised learning [master’s thesis]. Dublin, Ireland: University of Dublin, Trinity College; 2018. 1–86.
22. Jang H, Lee J. An empirical study on modelling and prediction of bitcoin prices with Bayesian neural networks based on blockchain information. IEEE Access. 2017;6:5427–5437. doi:10.1109/ACCESS.2017.2779181.
23. Rouhani S, Deters R. Performance analysis of Ethereum transactions in private blockchain. 8th IEEE International Conference on Software Engineering and Service Science (ICSESS). Beijing, China. 2017, Nov 24-26. 70–74. IEEE. doi:10.1109/ICSESS.2017.8342866.
24. Chan W, Olmsted A. Ethereum transaction graph analysis. 12^th International Conference for Internet Technology and Secured Transactions (ICITST). Cambridge, UK. 2017, Dec 11-14. 498–500. IEEE. doi:10.23919/ICITST.2017.8356459.
25. Georgoula I, Pournarakis D, Bilanakos C, Sotiropoulos D, Giaglis GM. Using time-series and sentiment analysis to detect the determinants of bitcoin prices. Mediterranean Conference on Information Systems (MCIS). Samos, Greece. 2015. 1–15.
26. Matta M, Lunesu I, Marchesi M. Bitcoin spread prediction using social and web search media. UMAP Workshops. 2015;1–15.
27. Wood G. Ethereum: A secure decentralised generalised transaction ledger. Ethereum Project Yellow Paper. 2014;1–41.
28. Gu B, Konana P, Liu A, Rajagopalan B, Ghosh J. Identifying information in stock message boards and its implications for stock market efficiency. Workshop on Information Systems and Economics. Los Angeles, CA; 2006.
29. Saad M, Choi J, Nyang D, Kim J, Mohaisen A. Toward characterizing blockchain-based cryptocurrencies for highly accurate predictions. IEEE Syst J. 2020;14(1):321–332. doi:10.1109/JSYST.2019.2927707.
30. Xia P, Wang H, Zhang B, Ji R, Gao B, Wu, Luo L, Xu G. Characterizing cryptocurrency exchange scams. Comput Sec. 2020;98:101993. doi:10.1016/j.cose.2020.101993.