Cryptocurrency Market Behavior: A Data Analytics Approach to Price Prediction

Abstract

With the rapid development of digital finance, cryptocurrencies have become an important component of the global financial market, and their price prediction has become a research hotspot with both theoretical and practical value. The high volatility, non-linearity and multi-factor driving characteristics of cryptocurrency prices make traditional financial prediction models difficult to achieve satisfactory results. This paper focuses on the research of cryptocurrency price prediction based on data analytics methods: first, it analyzes the multi-dimensional influencing factors of cryptocurrency market behavior, including on-chain data, macroeconomic indicators and social sentiment signals; second, it sorts out the application of mainstream data analytics models in price prediction, such as time series analysis, machine learning and deep learning algorithms; finally, it discusses the technical challenges faced by current prediction research, such as data noise interference and market black swan events, and proposes future optimization directions combined with emerging technologies such as federated learning and graph neural networks. This paper provides a systematic methodological framework for the research of cryptocurrency price prediction and the risk management of digital asset investment.