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Authors: Trubin A., Aleksahin A., Batishchev A., Filimonova E., Morozov A., Ozheredov V.     Published in № 3(99) 31 may 2022 year
Rubric: Models and methods

Building and analyzing a machine learning model for short-term bitcoin market forecasting based on recurrent neural networks

In this article, the construction and analysis of machine learning models were performed for short-term forecasting in the cryptocurrency market on the example of bitcoin – one of the most popular cryptocurrencies in the world. The initial data for the study leads to the conclusion that over the long period of its existence, bitcoin has shown a high degree of volatility, especially evident in comparison with traditional financial instruments. The article substantiates that this market is influenced by a multitude of factors. No one can say for sure what makes up the value of a particular cryptocurrency, as it involves a range of reasons, which cannot be fully taken into account. To overcome this problem, we have considered the principle of recurrent neural network. It is described why networks with memory are better at making predictions on the time series than conventional autoregressive model and standard forward propagation networks. The initial data processing algorithm and transformation methods are defined. The sample was reduced in order to increase the speed of the network, by reducing the number of recalculations of weights. The algorithm of the family of recurrent neural networks was built and trained to test the hypothesis about their better adaptivity due to short-term and long-term memory. The model is evaluated on the test data representing the bitcoin exchange rate for 2021–2022, since this period is characterized by high volatility. It is concluded that it is reasonable to use a similar type of models for short-term forecasting of cryptocurrency rates.

Key words

recurrent neural networks, time series prediction, cryptocurrency, bitcoin, LSTM

The author:

Trubin A.

Degree:

Cand. Sci. (Econ.), Associate Professor, Director of the Digital Economy Department, Synergy University

Location:

Moscow, Russia

The author:

Aleksahin A.

Degree:

Cand. Sci. (Ped.), Head of the Information Management and Information and Communication Technologies Department named after Professor V. V. Dik, Synergy University

Location:

Moscow, Russia

The author:

Batishchev A.

Degree:

Cand. Sci. (Econ.), Associate Professor, Head of the Artifi Intelligence and Data Analysis Department, Synergy University

Location:

Moscow, Russia

The author:

Filimonova E.

Degree:

Cand. Sci. (Eng.), Associate Professor, Digital Economy Department, Synergy University

Location:

Moscow, Russia

The author:

Morozov A.

Degree:

4th year Student in the direction of preparation 09.03.03 "Applied Informatics", Orel State University named after I. S. Turgenev

Location:

Orel, Russia

The author:

Ozheredov V.

Degree:

Cand. Sci. (Phys.-Math.), Associate Professor, Information Management and Information and Communication Technologies Department named after Professor V. V. Dik, Synergy University; Researcher, Space Research Institute of the Russian Academy of Sciences

Location:

Moscow, Russia