RTdetector

This repository supplements our paper "RTdetector: Deep Transformer Networks for Time Series Anomaly Detection based on Reconstruction Trend" .

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 SMD UCR MBA

Distribution rights to some datasets may not be available. Check the readme files in the ./data/ folder for more details. If you want to ignore a dataset, remove it from the above command to ensure that the preprocessing does not fail.

Result Reproduction

All RTdetector experiments are implemented in PyTorch on an NVIDIA GeForce RTX 2080 Ti GPU.

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

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

We provide a trained model for verification, which can be run directly

python3 main.py --model RTdetector --dataset <dataset> --test

where <model> can be either of 'RTdetector','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.