Enhance detect-anomalies with advanced ML algorithms (Isolation Forest, seasonal decomposition, predictive modeling) and schedule frequent runs via pg_cron. Updates include implementing new detectors, ensemble logic, and plumbing to run and expose results through the anomaly detection UI and data hooks.
Introduce statistical anomaly detection for metrics via edge function, hooks, and UI components. Adds detection algorithms (z-score, moving average, rate of change), anomaly storage, auto-alerts, and dashboard rendering of detected anomalies with run-once trigger and scheduling guidance.
