Authors: Liashenko, Doctor of Sciences (Economics), Professor, ORCID ID 0000-0002-0197-4179, Kravets, PhD in Physics and Math, Associate Professor, ORCID ID 0000-0003-4823-5143, Repetskiyi, Economist Taras Schevchenko National University of Kyiv, Kyiv, Ukraine

Abstract:Artificial neural networks are modern methods suitable for solving the problem of nonlinear dependency approximation, which is successfully applied in many fields. This paper compares the predictive capabilities of Back Propagation, Radial Basis Function, Extreme Learning Machine, and Long-Short Term Memory neural networks to determine which artificial intelligence algorithm is best for modeling the price of Bitcoin opening. The criterion for comparing network performance was the standard deviation, the mean absolute deviation, and the accuracy of predicting the direction of change of course. At the same time, in the study of time series, it is recommended to perform a comprehensive data analysis using appropriate networks, depending on the length of the series and the specificity of the database.

Key words: Artificial intelligence; back propagation; radial basis function; extreme learning machine; long-short term memory; bitcoin.

Received: 29/03/2020

1st Revision: 10/04/2020

Accepted: 20/04/2020



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