Awesome

Setup the computing environment

# Conda Installation
wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
bash miniconda.sh -b -p $HOME/miniconda
source "$HOME/miniconda/etc/profile.d/conda.sh"
hash -r
conda config --set always_yes yes --set changeps1 no
conda update -q conda
conda info -a
# Create the environment
conda create -n <name> python=3.8
conda activate <name>
# Install independencies
pip install -r requirements.txt

This repo is built upon Pytorch (deep neural networks) and PytorchGeometrics (graph learning). For PyTorch and PyG installation, please follow the guide from the websites. Make sure you install the matched the version of Pytorch for PyG.

Transformer benchmark architecture (Informer -> AAAI'21 best paper)

Our GTA's Transformer architecture is built on top of Informer who won the best paper award of AAAI'21. One may refer to the link to receive more details.

Usage

For training of GTA for deep anomaly detection (dad), please refer to main_gta_dad.py for more information. We also provided a more detailed and complete cli description for training the model:

python -u main_gta_dad.py --model <model> --data <data>
--root_path <root_path> --data_path <data_path> --features <features>
--target <target> --freq <freq> --checkpoints <checkpoints>
--seq_len <seq_len> --label_len <label_len> --pred_len <pred_len>
--enc_in <enc_in> --dec_in <dec_in> --c_out <c_out> --d_model <d_model>
--n_heads <n_heads> --e_layers <e_layers> --d_layers <d_layers>
--s_layers <s_layers> --d_ff <d_ff> --factor <factor> --dropout <dropout> 
--attn <attn> --embed <embed> --activation <activation>
--num_workers <num_workers> --train_epochs <train_epochs> --itr <itr>
--batch_size <batch_size> --patience <patience> --des <des>
--learning_rate <learning_rate> --loss <loss> --lradj <lradj>
--use_gpu <use_gpu> --gpu <gpu>

We would update the repo by providing a version that supports multi-gpus using Pytorch Lightning.

Citation

If you find this repository useful in your research, please consider citing the following paper:

@ARTICLE{zekaietal-gta-2021,
  author    = {Chen, Zekai and
               Chen, Dingshuo and
               Zhang, Xiao and 
               Yuan, Zixuan and 
               Cheng, Xiuzhen},
  journal   = {IEEE Internet of Things Journal}, 
  title     = {Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoT}, 
  year      = {2021},
  pages     = {1-1},
  doi       = {10.1109/JIOT.2021.3100509}}

Contact

If you have any questions, feel free to contact Zekai Chen through Email (zekai.chen@bms.com) or Github issues. Pull requests are highly welcomed!