A collection of popular anomaly detection methods (iid/point-based and time series) including active learning for anomaly detection/discovery, description for diversity/explanation/interpretability. With a deeper analysis of incorporating label feedback with ensemble and tree-based detectors. Includes results-plots and illustrations for most approaches covered. - View it on GitHub