A Python toolkit for rule-based/unsupervised anomaly detection in time series
Anomaly Detection Toolkit (ADTK) is a Python package for unsupervised / rule-based time series anomaly detection.
As the nature of anomaly varies over different cases, a model may not work universally for all anomaly detection problems. Choosing and combining detection algorithms (detectors), feature engineering methods (transformers), and ensemble methods (aggregators) properly is the key to build an effective anomaly detection model.
This package offers a set of common detectors, transformers and aggregators with unified APIs, as well as pipe classes that connect them together into models. It also provides some functions to process and visualize time series and anomaly events.
See https://adtk.readthedocs.io for complete documentation.
Prerequisites: Python 3.5 or later.
It is recommended to install the most recent stable release of ADTK from PyPI.
pip install adtk
Alternatively, you could install from source code. This will give you the latest, but unstable, version of ADTK.
git clone https://github.com/arundo/adtk.git cd adtk/ git checkout develop pip install ./
Please see Quick Start for a simple example.
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update unit tests as appropriate.
Please see Contributing for more details.
ADTK is licensed under the Mozilla Public License 2.0 (MPL 2.0). See the LICENSE file for details.