STUDYING MACHINE LEARNING METHODS FOR CRYPTOCURRENCY PRICE FORECASTING - Scientific conference
Main page
UA PL EN

Congratulation from Internet Conference!

Hello

Рік заснування видання - 2011

STUDYING MACHINE LEARNING METHODS FOR CRYPTOCURRENCY PRICE FORECASTING

15.09.2025 12:43

[1. Information systems and technologies]

Author: Volodymyr Tokariev, PhD, Associate Professor of the department of Information systems, Simon Kuznets Kharkiv national university of economics; Vadym Stekhnenko, master, department of Information systems, Simon Kuznets Kharkiv national university of economics


ORCID: 0000-0002-7143-6165 Volodymyr Tokariev

In the modern world of finance, cryptocurrency markets occupy a special place and have become the subject of close attention both among specialists and among the general public. The rapid development of these markets is observed, which occurs against the background of rapid technological progress and changes in consumer preferences. Cryptocurrencies, such as Bitcoin, Ethereum and others, have become not only an object of investment, but also a new form of financial assets influencing the global economy. Digital currency markets have become a place where both innovative technologies and traditional principles of money management meet. The growth of the capitalization of cryptocurrency markets and the growth of society's interest in these assets create new challenges and opportunities for the study of their nature, dynamics and influence on the global financial system.

The role of cryptocurrencies in the modern world of finance gives them importance both for the economic development of individual countries and for the formation of international financial relations. Their influence gradually expands to different spheres of activity, from trade and investment to financial intermediation and technological innovations. The variety of cryptocurrencies and their growth in value stimulate the search for new ways of using these assets. Cryptocurrencies have become not only a new form of digital assets, but also a subject of investment and trade, which creates a huge demand for analyzing and forecasting their prices. In this context, the use of AI becomes an important factor for achieving successful investment strategies and risk management. Artificial neural networks are used for predictive modeling, adaptive control, where they can be trained using a set of data. They are used to solve artificial intelligence problems.

Artificial neural networks consist of connected units or nodes called artificial neurons. They are connected by edges simulating synapses in the human brain. An artificial neuron receives signals from connected neurons, then processes them and sends a signal to other connected neurons. "Signal" is a real number. The output of each neuron is calculated using a non-linear function called the activation function. Neurons and edges usually have a weight that adjusts as the training progresses. The weight increases or decreases the signal strength when connected. Neurons are integrated into layers. Different layers can perform different transformations of their input data. A network is usually called a deep neural network if it has at least 2 hidden layers. Signals pass from the first layer (input layer) to the last layer, passing through several intermediate layers fig.1.




Fig.1.Artificial neural network diagram

Література:

1. Токарєв В., Ільїна І., Шевченко І., Гриценко І. Про один підхід до рішення асиметричної TSP - задачі при B2C доставках за допомогою платформи "Swarm-bot" - system у фізичному неорганізованому середовищі. Системи управління, навігації та зв'язку, 2023. - №4(74), cc. 110-113.

2. L.E. Gryzun, V.V. Tokariev. Mobile applications design for digital education: IT-students’ engagement experience on conditions of online learning the course "Mobile technologies". Proceedings of the 2nd Workshop on Digital Transformation of Education, co-located with the 18th International Conference on ICT in Education, Research, and Industrial Applications: (ICTERI 2023). CEUR Workshop Proceedings, 2023. Ivano-Frankivsk. Ukraine, рр. 110-123..

3. Koshovyi M. D., Pylypenko O. T., Ilyina I. V., Tokarev V. V. Growing tree method for optimization of multifactorial experiments, Radio Electronics, Computer Science, Control, 2023. - № 3, pp. 55-61.

4. Koshevoy N., Ilina I., Tokariev V., Malkova A., Muratov V. Implementation Of The Gravity Search Method For Optimization By Cost Expenses Of Plans For Multifactorial Experiments. Radioelectronic and Computer Systems, 2023. – Vol. 1(105), pp. 23-32. 

5. Ільїна І.В., Токарєв В.В., Яковлєв А.В., Шевченко І.І. Використання системи підтримки прийняття рішень для організації гуманітарної логістики, Системи управління, навігації та зв'язку, 2024, №1(75), cc. 88-91.



Creative Commons Attribution Ця робота ліцензується відповідно до Creative Commons Attribution 4.0 International License
допомога Знайшли помилку? Виділіть помилковий текст мишкою і натисніть Ctrl + Enter

Another articles in this section

Сonferences

Conference 2025

Conference 2024

Conference 2023

Conference 2022

Conference 2021



Міжнародна інтернет-конференція з економіки, інформаційних систем і технологій, психології та педагогіки

Наукова спільнота - інтернет конференції

:: LEX-LINE :: Юридична лінія

Інформаційне суспільство: технологічні, економічні та технічні аспекти становлення