Modern Approaches to the Intelligent Analysis of the Cryptocurrency Market
Keywords:
cryptocurrency, dataset, machine learning, deep learning, neural networks, AI systems, intelligent data analysis, database, programmingAbstract
The article addresses the challenges of intelligent analysis in the cryptocurrency market and, due to the limitations of traditional financial analysis methods, substantiates the need for the implementation of artificial intelligence (AI) systems. These systems are essential for processing large volumes of heterogeneous data, improving forecasting accuracy, and enhancing decision-making efficiency, particularly in light of the market’s high volatility, dynamic nature, and complexity. The review of recent studies on AI-based models for cryptocurrency price prediction reveals a growing demand for the development of flexible, hybrid architectures that integrate intelligent approaches with classical analytical techniques. The paper analyses the architecture of a hybrid AI system, encompassing modules for data collection, pre-processing, model training, and results visualization. It details the handling of various data types, including time series, on-chain metrics, and textual information. Within the framework of the empirical study, a comparative analysis of three models — ARIMA, Random Forest Regressor, and LSTM — was conducted using an open dataset of historical Bitcoin prices. Special attention is given to feature engineering, with the introduction of additional parameters that significantly improved model training performance. Based on the research findings, a concept of a hybrid multi-layered AI system is proposed. This system includes data acquisition and normalization modules, a computation block utilizing GPU/TPU resources for model training, and dashboards and APIs for result visualization and integration with exchange platforms. The proposed conceptual AI model can serve as a foundation for practical implementation in investor-oriented and financial-analytical platforms. It may attract the interest of fintech developers, traders, and researchers in the digital economy, paving the way for high-precision forecasting tools and automated decision-making solutions in the cryptocurrency market.
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