I’m happy to announce, that we finally released MultivariateAnomalies.jl - A julia package for detecting multivariate anomalies.
Keywords: Novelty detection, Anomaly Detection, Outlier Detection, Statistical Process Control, Process Monitoring
Currently implemented are some anomaly detection algorithms, specifically:
- Kernel density estimation,
- two k-nearest neighbours approaches,
- Hotelling’s T^2,
- a recurrence approach,
- Support vector data description and
- Kernel Null Foley Summon Transform
A comparison of the implemented algorithms on Earth observation like data can be found here: doi:10.5194/esd-8-677-2017
Detect anomalies in your data with easy to use high level functions or individual anomaly detection algorithms.
Feature Extraction: Preprocess your data by extracting relevant features
Postprocessing: Postprocess your anomaly scores, by computing their quantiles or combinations of several algorithms (ensembles).
AUC: Compute the area under the curve as external evaluation metric of your anomaly scores.
Online Algorithms: Selected anomaly detection algorithms tuned for little memory allocation (to be extended).
The Documentation is available here