Дослідження методів прогнозування і аналітики кривих ціноутворення цифрових активів

Abstract

The research investigates various methods for predicting and analyzing the price movements of digital assets. The study examines the effectiveness of traditional time series models and machine learning algorithms in forecasting cryptocurrency prices. The author also explore the role of social media sentiment and other external factors in affecting the prices of digital assets. The findings suggest that machine learning models, particularly neural networks, outperform traditional methods in predicting price movements. The study highlights the importance of incorporating external factors and continuous model refinement in accurately forecasting the prices of digital assets. Overall, the research provides valuable insights into the emerging field of cryptocurrency forecasting and analytics.