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A Python toolkit for rule-based/unsupervised anomaly detection in time series
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README.md

Anomaly Detection Toolkit (ADTK)

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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://arundo-adtk.readthedocs-hosted.com for complete documentation.

Installation

Prerequisites: Python 3.5 or later.

It is recommended to use pip for installation.

pip install adtk

Alternatively, you could install from source code:

git clone https://github.com/arundo/adtk.git
cd adtk/
pip install ./

Examples

Please see Quick Start for a simple example.

For more detailed examples of each module of ADTK, please refer to Examples section in the documentation.

Contributing

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 tests as appropriate.

License

ADTK is licensed under the Mozilla Public License 2.0 (MPL 2.0). See the LICENSE file for details.

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