timeeval-gutenTAG 0.2.0

A good Timeseries Anomaly Generator.

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, and 3.10; 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 in the middle and the end of the series. You can use the following content:

   timeseries:
   - name: demo
     length: 1000
     channels: 1
     semi-supervised: true
     supervised: true
     base-oscillation:
       kind: sine
       frequency: 4.0
       amplitude: 1.0
       variance: 0.05
       anomalies:
       - position: middle
         length: 50
         kinds:
         - kind: pattern
           parameters:
           sinusoid_k: 10.0
           cbf_pattern_factor: 1.0
       - position: end
         length: 10
         kinds:
         - kind: amplitude
           parameters:
           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:

tbd

To-Do

• negation anomaly (does a pattern not appear)