Metadata-Version: 2.1
Name: timeeval-GutenTAG
Version: 1.4.2
Summary: A good Timeseries Anomaly Generator.
Home-page: https://github.com/TimeEval/gutentag
Author-email: Phillip Wenig <phillip.wenig@hpi.de>, Sebastian Schmidl <sebastian.schmidl@hpi.de>
License: MIT License
        
        Copyright (c) 2020-2021 Phillip Wenig and Sebastian Schmidl
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
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        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Typing :: Typed
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE

<div align="center">
    <img width="400px" src="https://github.com/TimeEval/gutentag/raw/main/logo_transparent.png" alt="TimeEval logo"/>
    <p>
    A good <strong>T</strong>imeseries <strong>A</strong>nomaly <strong>G</strong>enerator.
    </p>

[![CI](https://github.com/TimeEval/gutentag/actions/workflows/build.yml/badge.svg)](https://github.com/TimeEval/gutentag/actions/workflows/build.yml)
[![codecov](https://codecov.io/gh/TimeEval/gutentag/branch/main/graph/badge.svg?token=6QXOCY4TS2)](https://codecov.io/gh/TimeEval/gutentag)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![PyPI package](https://badge.fury.io/py/timeeval-gutenTAG.svg)](https://badge.fury.io/py/timeeval-gutenTAG)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
![python version 3.7|3.8|3.9|3.10|3.11](https://img.shields.io/badge/python-3.7%20%7C%203.8%20%7C%203.9%20%7C%203.10%20%7C%203.11-blue)
[![Downloads](https://pepy.tech/badge/timeeval-gutentag)](https://pepy.tech/project/timeeval-gutentag)

</div>

GutenTAG is an extensible tool to generate time series datasets with and without anomalies.
A GutenTAG time series consists of a single (univariate) or multiple (multivariate) channels containing a base oscillation with different anomalies at different positions and of different kinds.

[![base-oscillations](https://img.shields.io/badge/base_oscillations-11-3a4750?style=for-the-badge)](./doc/introduction/base-oscillations.md)
[![base-oscillations](https://img.shields.io/badge/anomaly_types-10-f6c90b?style=for-the-badge)](./doc/introduction/anomaly-types.md)
[![base-oscillations](https://img.shields.io/badge/add--ons-1-f64e8b?style=for-the-badge)](./doc/advanced-features.md)

[![base-oscillations](https://img.shields.io/badge/easy_config-YAML-3a4750?style=for-the-badge)](./doc/usage.md)

## tl;dr

1. Install GutenTAG from [PyPI](https://pypi.org/project/timeeval-gutenTAG/):

   ```sh
   pip install timeeval-gutenTAG
   ```

   GutenTAG supports Python 3.7, 3.8, 3.9, 3.10, and 3.11; all other [requirements](./requirements.txt) are installed with the pip-call above.

2. Create a generation configuration file [`example-config.yaml`](./generation_configs/example-config.yaml) with the instructions to generate a single time series with two anomalies:
   A _pattern_ anomaly in the middle and an _amplitude_ anomaly at the end of the series.
   You can use the following content:

   ```yaml
   timeseries:
   - name: demo
     length: 1000
     base-oscillations:
     - kind: sine
       frequency: 4.0
       amplitude: 1.0
       variance: 0.05
     anomalies:
     - position: middle
       length: 50
       kinds:
       - kind: pattern
         sinusoid_k: 10.0
     - position: end
       length: 10
       kinds:
       - kind: amplitude
         amplitude_factor: 1.5
   ```

3. Execute GutenTAG with a seed and let it plot the time series:

   ```bash
   gutenTAG --config-yaml example-config.yaml --seed 11 --no-save --plot
   ```

   You should see the following time series:

   ![Example unsupervised time series with two anomalies](https://github.com/TimeEval/gutentag/raw/main/example-ts.png)

## Documentation

GutenTAG's documentation can be found [here](doc/index.md).

## Citation

If you use GutenTAG in your project or research, please cite our demonstration paper:

> Phillip Wenig, Sebastian Schmidl, and Thorsten Papenbrock.
> TimeEval: A Benchmarking Toolkit for Time Series Anomaly Detection Algorithms. PVLDB, 15(12): 3678 - 3681, 2022.
> doi:[10.14778/3554821.3554873](https://doi.org/10.14778/3554821.3554873)

```bibtex
@article{WenigEtAl2022TimeEval,
  title = {TimeEval: {{A}} Benchmarking Toolkit for Time Series Anomaly Detection Algorithms},
  author = {Wenig, Phillip and Schmidl, Sebastian and Papenbrock, Thorsten},
  date = {2022},
  journaltitle = {Proceedings of the {{VLDB Endowment}} ({{PVLDB}})},
  volume = {15},
  number = {12},
  pages = {3678 -- 3681},
  doi = {10.14778/3554821.3554873}
}
```

## Contributing

We welcome contributions to GutenTAG.
If you have spotted an issue with GutenTAG or if you want to enhance it, please open an issue first.
See [Contributing](CONTRIBUTING.md) for details.
