Toward Characterizing Blockchain-Based Cryptocurrencies for Highly Accurate Predictions
- Authors
- Saad, Muhammad; Choi, Jinchun; Nyang, DaeHun; Kim, Joongheon; Mohaisen, Aziz
- Issue Date
- Mar-2020
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Biological system modeling; Bitcoin; Bitcoin; Blockchain; Blockchain; Ethereum; Indexes; Market research; prediction; Predictive models
- Citation
- IEEE Systems Journal, v.14, no.1, pp 321 - 332
- Pages
- 12
- Journal Title
- IEEE Systems Journal
- Volume
- 14
- Number
- 1
- Start Page
- 321
- End Page
- 332
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/37876
- DOI
- 10.1109/JSYST.2019.2927707
- ISSN
- 1932-8184
1937-9234
- Abstract
- Recently, the Blockchain-based cryptocurrency market witnessed enormous growth. Bitcoin, the leading cryptocurrency, reached all-time highs many times over the year leading to speculations to explain the trend in its growth. In this article, we study Bitcoin and Ethereum and explore features in their network that explain their price hikes. We gather data and analyze user and network activity that highly impact the price of these cryptocurrencies. We monitor the change in the activities over time and relate them to economic theories. We identify key network features that help us to determine the demand and supply dynamics in a cryptocurrency. Finally, we use machine learning methods to construct models that predict Bitcoin price. Based on our experimental results using two large datasets for validation, we confirm that our approach provides an accuracy of up to 99% for Bitcoin and Ethereum price prediction in both instances. IEEE
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Collections - College of ICT Engineering > School of Computer Engineering > 1. Journal Articles
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