Contextual Performance Anomaly Detection Framework

Design an advanced anomaly detection system that can identify performance irregularities across complex distributed systems using contextual and temporal analysis. The framework should leverage machine learning models to distinguish between genuine performance issues and normal system variations, with automatic root cause identification and predictive remediation suggestions. Demonstrate the system's ability to handle high-dimensional telemetry data from diverse sources.