TranAD

This repository supplements our paper "TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data" accepted in VLDB 2022. This is a refactored version of the code used for results in the paper for ease of use. Follow the below steps to replicate each cell in the results table.

Results

Installation

This code needs Python-3.7 or higher.

pip3 install torch==1.8.1+cpu torchvision==0.9.1+cpu torchaudio===0.8.1 -f https://download.pytorch.org/whl/torch_stable.html
pip3 install -r requirements.txt

Dataset Preprocessing

Preprocess all datasets using the command

python3 preprocess.py SMAP MSL SWaT WADI MSDS SMD MSDS UCR MBA NAB

Distribution rights to some datasets may not be available. Check the readme files in the ./data/ folder for more details.

Result Reproduction

To run a model on a dataset, run the following command:

python3 main.py --model <model> --dataset <dataset> --retrain

where <model> can be either of 'TranAD', 'GDN', 'MAD_GAN', 'MTAD_GAT', 'MSCRED', 'USAD', 'OmniAnomaly', 'LSTM_AD', and dataset can be one of 'SMAP', 'MSL', 'SWaT', 'WADI', 'SMD', 'MSDS', 'MBA', 'UCR' and 'NAB. To train with 20% data, use the following command

python3 main.py --model <model> --dataset <dataset> --retrain --less

For ablation studies, use the following models: 'TranAD_SelfConditioning', 'TranAD_Adversarial', 'TranAD_Transformer', 'TranAD_Basic'.

The output will provide anomaly detection and diagnosis scores and training time. For example:

$ python .\main.py --model TranAD --dataset SMAP --retrain 
Using backend: pytorch
Creating new model: TranAD
Training TranAD on SMAP
Epoch 0,        L1 = 0.09839354782306504
Epoch 1,        L1 = 0.039524692888342115
Epoch 2,        L1 = 0.022258711623482686
Epoch 3,        L1 = 0.01833707226553135
Epoch 4,        L1 = 0.016330517334598792
100%|███████████████████████████████████████████████████████████████████| 5/5 [00:03<00:00,  1.57it/s]
Training time:     3.1920 s
Testing TranAD on SMAP
{'FN': 0,
 'FP': 182,
 'Hit@100%': 1.0,
 'Hit@150%': 1.0,
 'NDCG@100%': 0.9999999999999999,
 'NDCG@150%': 0.9999999999999999,
 'TN': 7575,
 'TP': 748,
 'f1': 0.8915325929177795,
 'precision': 0.8043010666204187,
 'recall': 0.9999999866310163,
 'threshold': 0.16133320075167037}

All outputs can be run multiple times to ensure statistical significance.

Cite this work

Our paper is available on arxiv: https://arxiv.org/abs/2201.07284. If you use this work, please cite using the following bibtex entry.

@article{tuli2022tranad,
  title={{TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data}},
  author={Tuli, Shreshth and Casale, Giuliano and Jennings, Nicholas R},
  journal={arXiv preprint arXiv:2201.07284},
  year={2022}
}

License

BSD-3-Clause. Copyright (c) 2022, Shreshth Tuli. All rights reserved.

See License file for more details.