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Anomaly Detection

Anomstack uses PyOD (Python Outlier Detection) to detect anomalies in your metrics. This section explains how the anomaly detection works and how to configure it.

How It Works​

Anomstack's anomaly detection process:

  1. Ingests metric data
  2. Preprocesses the data
  3. Trains detection models
  4. Scores new data points
  5. Identifies anomalies

Configuration​

You can configure anomaly detection through:

  • Model selection
  • Training parameters
  • Scoring thresholds
  • Custom preprocessing

Models​

Anomstack supports various anomaly detection models:

  • Isolation Forest
  • Local Outlier Factor
  • One-Class SVM
  • And more...

Examples​

Coming soon...

Best Practices​

Coming soon...