Predicting Bitcoin Volatility Using Machine Learning Algorithms and Blockchain Technology

Mijoska, Mimoza and Ristevski, Blagoj and Savoska, Snezana and Trajkovik, Vladimir (2022) Predicting Bitcoin Volatility Using Machine Learning Algorithms and Blockchain Technology. In: The 15-th Conference on Information Systems and Grid Technologies ISGT 2022, May 27-28, 2022, Sofia, Bulgaria.

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Abstract

Blockchain technology has the potential to be applied in a variety of areas of our daily life. Blockchain is the foundation of cryptocurrency, but the applications of blockchain technology are much more expansive. This technology is considered to be a revolutionary solution for the financial industry. Also, it can be successfully applied in scenarios involving data validation, auditing, and sharing. On the other hand, machine learning is one of the most noticeable technologies in recent years. Both technologies are data-driven, and thus there are rapidly growing interests in integrating them for more secure and efficient data sharing and analysis. This paper shows how these two technologies, blockchain and machine learning, can be combined in predicting bitcoin volatility. To analyze and predict bitcoin volatility, bitcoin data from real-time series and random forests as a machine learning algorithm were used. When predicting bitcoin volatility, low statistical errors were obtained in the training and test set. This confirms that the forecasting model is well designed.

Item Type: Conference or Workshop Item (Paper)
Subjects: Scientific Fields (Frascati) > Natural sciences > Computer and information sciences
Scientific Fields (Frascati) > Engineering and Technology > Electrical engineering, electronic engineering,information engineering
Divisions: Faculty of Information and Communication Technologies
Depositing User: Prof. d-r. Blagoj Ristevski
Date Deposited: 24 Aug 2022 15:22
Last Modified: 24 Aug 2022 15:22
URI: http://eprints.uklo.edu.mk/id/eprint/7064

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