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Implement anomaly detection and forecasting features
Ananth Shyam- Added new documentation for Area 4: Anomaly Detection & Forecasting, detailing its purpose, implementation, and design principles. - Created the anomaly detection module with the following components: - `src/anomaly/__init__.py`: Initialization file for the anomaly module. - `src/anomaly/db.py`: Supabase persistence layer for anomaly signals and query events. - `src/anomaly/notifier.py`: In-process WebSocket broadcast bridge for real-time notifications of critical/high anomaly signals. - `src/anomaly/router.py`: FastAPI router for the anomaly detection API endpoints. - `src/anomaly/tasks.py`: Celery tasks for running anomaly detection algorithms, including Z-score detection, staleness scoring, and dependency risk modeling. - Developed SQL migration script (`supabase/anomaly_migration.sql`) to create necessary tables for storing query events and anomaly signals.451d52a