Leveraging Machine Learning for Anomaly Detection Methods in Cryptocurrency: A Data-Driven Study

Abstract

Cryptocurrencies like Bitcoin and Ethereum have experienced remarkable growth but also high volatility, making anomaly detection crucial for predictive modeling and risk management. This study evaluates three unsupervised machine learning algorithms - Local Outlier Factor, One-Class Support Vector Machine, and Isolation Forest - for detecting anomalies in Bitcoin and Ethereum prices and returns from January 2017 to May 2024. Local Outlier Factor is benchmarked against One-Class Support Vector Machine and Isolation Forest using mean absolute error (MAE), root mean squared error (RMSE), and explained variance score (EVS) as performance metrics. Results demonstrate Local Outlier Factor outperforms One-Class Support Vector Machine and Isolation Forest in detecting return anomalies for both cryptocurrencies. While Local Outlier Factor and Isolation Forest exhibit similar global anomaly detection capability, Local Outlier Factor achieves lower RMSE, indicating superior efficiency in capturing cryptocurrency market dynamics. One-Class Support Vector Machine lagged the other two methods across all metrics. The findings highlight the efficacy of density-based local outlier detection techniques like Local Outlier Factor for cryptocurrency applications. This study provides valuable insights for developing robust anomaly detection systems to enhance risk management and trading strategies in the highly volatile cryptocurrency market.