Build a Server-Metrics Anomaly Detector

• ✓Show why a fixed threshold fires every evening on a metric with a daily rhythm, and replace it with a seasonal robust baseline
• ✓Detect point, contextual, collective and cross-metric anomalies in simulated server metrics
• ✓Fit Isolation Forest and Local Outlier Factor on window features and show how much contamination changes the result
• ✓Score a detector by incident, not by point, and measure how late it notices
• ✓Set an alert threshold from an alert budget, merge alerts into incidents, and test the detector against a shifted daily pattern
• ✓Write an operating note that states what normal means and when to refit

Build the alerting an operations or fraud team runs on a metric with a daily rhythm — learn what is normal at each time of day, catch what a fixed threshold cannot, and set the threshold from the number of alerts a person will read.