High-Performance Time Series Anomaly Detection System

Create an advanced anomaly detection system for financial trading data using multiple statistical and machine learning techniques. Implement Z-score, Isolation Forest, and LSTM autoencoder methods to detect complex temporal anomalies with minimal false positives. Design a modular architecture that supports real-time streaming data, handles high-frequency financial instruments, and provides interpretable deviation metrics.