Multivariate Anomaly Detection with GAN (MAD-GAN) PyTorch modern implementation.
This implementation is based on the model described in the MAD-GAN paper (https://arxiv.org/pdf/1901.04997.pdf).
To train the MAD-GAN neural network you need a preprocessed dataset in CSV format (more formats will come soon).
The CSV should look like this:
feature1,feature2,feature3,featureN
0.1,-0.2,-0.7,0.8
0.1,-0.2,-0.7,0.8
0.1,-0.2,-0.7,0.8
...
Note that for now time based windows are not supported (support will drop soon)
Then to train the model you just run the following CLI command.
$ python -m madgan train \
data/dataset.csv
--batch-size 32 \
--epochs 8 \
--model-dir models/madgan # Training checkpoints will be stored here
TBD
[1] MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks