# ANN: MultivariateAnomalies.jl - package for detecting multivariate anomalies or novelty detection

**URL:** <https://discourse.julialang.org/t/ann-multivariateanomalies-jl-package-for-detecting-multivariate-anomalies-or-novelty-detection/6087>\
**Category:** Community\
**Tags:** package, announcement\
**Created:** [September 26, 2017, 1:11pm UTC](https://discourse.julialang.org/t/ann-multivariateanomalies-jl-package-for-detecting-multivariate-anomalies-or-novelty-detection/6087 "2017-09-26T13:11:20Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![milan](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/milan/32/2009_2.png) [@milan](https://discourse.julialang.org/u/milan)\
**Post date:** [September 26, 2017, 1:11pm UTC](https://discourse.julialang.org/t/ann-multivariateanomalies-jl-package-for-detecting-multivariate-anomalies-or-novelty-detection/6087/1 "2017-09-26T13:11:21Z")

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I’m happy to announce, that we finally released [MultivariateAnomalies.jl](https://github.com/milanflach/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](https://doi.org/10.5194/esd-8-677-2017)

## Package Features

- 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).

## Documentation

The Documentation is available [here](https://milanflach.github.io/MultivariateAnomalies.jl/latest/man/DetectionAlgorithms.html)