Ethereum Price Prediction using Extra Trees and Random Forest Regressors

Volume: 10 | Issue: 02 | Year 2024 | Subscription
International Journal of Distributed Computing and Technology
Received Date: 05/01/2024
Acceptance Date: 05/26/2024
Published On: 2024-11-12
First Page:
Last Page:

Journal Menu

By: Sushiladevi B. Vantamuri, Shreyas Salimath, Megha Mantur, Rutuja Bhosale, and Misba Sindoli

Abstract

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 showcases 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. Ultimately, 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, Extra Trees, Random Forest Regressors.

Loading

Citation:

How to cite this article: Sushiladevi B. Vantamuri, Shreyas Salimath, Megha Mantur, Rutuja Bhosale, and Misba Sindoli, Ethereum Price Prediction using Extra Trees and Random Forest Regressors. International Journal of Distributed Computing and Technology. 2024; 10(02): -p.

How to cite this URL: Sushiladevi B. Vantamuri, Shreyas Salimath, Megha Mantur, Rutuja Bhosale, and Misba Sindoli, Ethereum Price Prediction using Extra Trees and Random Forest Regressors. International Journal of Distributed Computing and Technology. 2024; 10(02): -p. Available from:https://journalspub.com/publication/ijdct-v10i02-11888/
Full Text

Refrences:

  1. Vijaya Kumar T, S. Santhi, K. G. Shanthi, Gokila M, “Cryptocurrency Price Predictionusing LSTM and Recurrent Neural Networks”, IEEE, https://ieeexplore.ieee.org/document/10141048
  2. Nishant Jagannath, “An On-Chain Analysis-Based Approach to Predict EthereumPrices”, IEEE Access, 2021. https://ieeexplore.ieee.org/iel7/6287639/6514899/09650873.pdf
  3. Nakamoto, “Bitcoin: A Peer-to-Peer Electronic Cash System”, 2021. https://bitcoin.org/bitcoin.pdf
  4. M. Khedr, I. Arif, V. P. Raj, M. El-Bannany, S. M. Alhashmi and M. Sreedharan, “Cryptocurrency price prediction using traditional statistical and machine-learning techniques: A survey”, Intell. Syst. Accounting Finance Manage, 2021. https://ideas.repec.org/a/wly/isacfm/v28y2021i1p3-34.html
  5. Jagannath, T. Barbulescu, K. M. Sallam, I. Elgendi, A. A. Okon, B. Mcgrath, A. Jamalipour, and K. Munasinghe, “A self- adaptive deep learning-based algorithm for predictive analysis of bitcoin price”, IEEE Access, 2021.
  6. Ahmed Khedr, Pravija Raj P V, “Cryptocurrency price prediction using traditional statistical and machine‐learning techniques: A survey”, ResearchGate, 2021.
  7. Sumit Biswas, Mohandas Pawar, Sachin Badole, Nachiket Galande, “Cryptocurrency Price Prediction Using Neural Networks and Deep Learning”, IEEE Access,
  8. Agis Politis, Katerina Doka, Nectarios koziris, “Ether Price Prediction using AdvancedDeep Learning Models”, IEEE,
  9. Jay, V. Kalariya, P. Parmar, S. Tanwar, N. Kumar, and M. Alazab, “Stochastic neuralnetworks for cryptocurrency price prediction”, IEEE Access, 2020.
  10. Harry Kalodner kalodner, Steven Goldfeder, Alishah Chator, “BlockSci: Design andapplications of a blockchain analysis platform”, ResearchGate,
  11. Xiang, M. Wang and W. Fan, “A permissioned blockchain-based identitymanagement and user authentication scheme for E-health systems”, IEEE Access, 2020.
  12. Bartoletti, S. Carta, T. Cimoli and R. Saia, “Dissecting Ponzi schemes on Ethereum: Identification analysis and impact”, Future Gener. Computer System, 2020.
  13. Zheshi Chen, Chunhong Li, Wenjun Sun, “Bitcoin Price Prediction using Machine Learning: An approach to sample dimension engineering”, ScienceDirect,
  14. M S Bhargavi, Sushmitha M Katti, Shilpa M, Vaishnavi P Kulkarni, Supraja Prasad, “Transactional Data Analytics for Inferring Behavioural Traits in Ethereum Blockchain Network”, IEEE,
  15. Poongodi, Ashutosh Sharma, Varadarajan Vijayakumar, Vaibhav Bhardwaj, Abhinav Parkash Sharma, Razi Iqbal, et al., “Prediction of the price of Ethereum blockchain cryptocurrency in an industrial finance system”, Computers and Electrical Engineering, 2020.
  16. E. Livieris, E. Pintelas, S. Stavroyiannis and P. Pintelas, “Ensemble deep learning models for forecasting cryptocurrency time-series”, Algorithms, 2020.
  17. Zheng, Z. Zheng, J. Wu, and H.-N. Dai, “XBlock-ETH: Extracting and exploring blockchain data from Ethereum”, IEEE, 2020.
  18. Chen, Z. Zheng, E. C.-H. Ngai, P. Zheng, and Y. Zhou, “Exploiting blockchain data to detect smart Ponzi schemes on Ethereum”, IEEE Access, 2019.
  19. Alexander Brauneis, Roland Mestel, “Price Discovery of cryptocurrency: Bitcoin andBeyond”, ScienceDirect, 2018.
  20. McNally S, Roche J, Caton S. Predicting the price of bitcoin using machine learning. In2018 26th euromicro international conference on parallel, distributed and network-based processing (PDP) 2018 Mar 21 (pp. 339-343). IEEE.
  21. O’Kane Eibhlínn, detecting patterns in the Ethereum transactional data using unsupervised learning, 2018.
  22. Jang and J. Lee, “An empirical study on modelling and prediction of bitcoin prices with Bayesian neural networks based on blockchain information”, IEEE Access, 2018.
  23. Sara Rouhani and Ralph Deters, “Performance analysis of Ethereum transactions in private blockchain”, the proceedings of 2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS), 2017.
  24. Wren Chan and Aspen Olmsted, “Ethereum transaction graph analysis”, the proceedings of 2017 12th International Conference for Internet Technology and Secured Transactions (ICITST),2017.
  25. George Giaglis, Christos Bilanakos, “Using Time Series and Sentiment Analysis to Detect the Determinants of Bitcoin Prices”, ResearchGate, 2015.
  26. Martina Matta, Maria Llari Lunesu, “Bitcoin Spread Prediction using Social and WebSearch Media”, ResearchGate, 2015.
  27. Gavin Wood, “Ethereum: A secure decentralised generalised transaction ledger”, Ethereum project yellow paper, 2014.
  28. Bin Gu, Prabhudev Konana, Alex Liu, Balaji Rajagopalan, and Joydeep Ghosh, “Identifying Information in Stock Message Boards and Its Implications for Stock Market Efficiency”, ResearchGate,2006.
  29. Saad, J. Choi, D. Nyang, J. Kim and A. Mohaisen, “Toward characterizing blockchain-based cryptocurrencies for highly accurate predictions”, IEEE Syst. J, 2020.
  30. Xia, H. Wang, B. Zhang, R. Ji, B. Gao, L. Wu, X. Luo, and Xu, “Characterizing cryptocurrency exchange scams”, ScienceDirect, 2020

 Previous Article
Next Article