Advanced Financial Time Series Anomaly Detection System

Create a sophisticated financial time series anomaly detection system using advanced statistical and machine learning techniques. Implement multiple detection algorithms including isolation forests, autoencoders, and Gaussian mixture models to identify complex market anomalies across different asset classes. Develop a modular framework that can be easily extended to new data sources, with real-time processing capabilities and automated alerting mechanisms.