This article presents a comprehensive study of modern methods for analyzing and forecasting cryptocurrency market dynamics. The author examines the evolution of the cryptocurrency market from a niche technological innovation to a significant segment of the global financial system, characterized by high volatility and sensitivity to a wide range of external factors. The research is based on an analysis of historical data from leading cryptocurrencies for the period 2015-2025 and systematization of recent scientific publications. The historical price dynamics of leading cryptocurrencies (Bitcoin, Ethereum and Litecoin) were analyzed for the period 2015–2025, confirming exponential market growth alongside extremely high volatility and cyclical fluctuations. Special attention is paid to identifying cyclical patterns and factors determining price dynamics. A SWOT analysis of the market was conducted, which allowed systematizing internal strengths and weaknesses, as well as external opportunities and threats. The main emphasis is placed on comparative analysis of forecasting models from classical statistical methods to modern machine learning and deep learning approaches. The author demonstrates evolution from simple linear models to complex neural networks that better account for the nonlinear nature of the market. The main classes of forecasting model are categorized, ranging from classical statistical approaches (e.g. ARIMA and GARCH) to modern machine learning and deep learning methods (e.g. LSTM, GRU and Transformers). The study reveals that deep learning models demonstrate higher accuracy on short- and medium-term forecasting horizons, however long-term forecasting remains a challenging task due to the influence of fundamental factors. The work has practical significance for investors, traders and analysts, providing structured information about market functioning features and tools for its analysis. The research contributes to understanding the complex dynamics of cryptocurrency markets and provides guidance for selecting appropriate analytical methods based on specific forecasting objectives and time horizons.
Valeriia Yu. Kochorba (Wed,) studied this question.