A good Timeseries Anomaly Generator.

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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.

tl;dr

1. Install GutenTAG from PyPI:

   pip install timeeval-gutenTAG

   GutenTAG supports Python 3.7, 3.8, 3.9, 3.10, and 3.11; all other requirements are installed with the pip-call above.

2. Create a generation configuration file 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:

   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:

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

   You should see the following time series:

   

Documentation

GutenTAG's documentation can be found here.

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

@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 for details.