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Outline {-}

  1. Introduction
  2. Tools
  3. Parametric methods
  4. Quantile and depth-based methods
  5. Density-based methods
  6. Distance-based methods
  7. High-dimensional methods
  8. Non-Euclidean spaces
  9. Time series anomalies
  10. Spatial, network and graph data
  11. Functional data (see https://github.com/otsegun/fdaoutlier)
  12. Video data
  13. Categorical and text data
  14. Distributional observations
  15. Deep learning for anomaly detection
  16. Applications: network intrusion, fraud detection
  17. Handling very large data sets

To include somewhere:

  • Isolation forests??
  • subspace, correlation-based and tensor-based methods
  • Cluster analysis?
    • Ensemble methods

Task view {-}

Additional Examples {-}

Collections of anomalous data {-}