[SESSION_127] AnomalyDetectionAgent: Implement Baseline Adaptation (Phase 23)

Feature: Baseline Adaptation and Learning

Component: AnomalyDetectionAgent (/agents/specialized/anomaly_detection_agent/)
Severity: 🔴 CRITICAL BLOCKER
Session: SESSION 127 (Definition of Done)
Current Status: Thresholds hardcoded, no updates from data
Target Phase: Phase 23

Context

Production anomaly detection requires adaptive baselines that learn from environment-specific patterns. Current hardcoded thresholds fail as data distributions change, causing false positive rate to rise over time.

Requirements

• Daily baseline recalculation
• Z-score threshold adaptation
• IQR quantile updates
• Seasonal pattern detection
• Drift handling for concept shift

Impact

Without this feature:

• False positive rate increases over time
• Cannot adapt to environment changes
• Degrades to useless after weeks
• No learning from historical data

Effort Estimate

• Baseline learning: 6-8 hours
• Seasonal decomposition: 2-3 hours
• Testing: 2-3 hours
• Total: 8-10 hours

Technical Approach

• Rolling window statistics
• Seasonal decomposition (STL)
• Drift detection (Adwin algorithm)
• Adaptive threshold updates
• Historical baseline tracking

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Generated by SESSION 127 FASE 2.7