The goal of this repository is to provide a benchmarking pipeline for anomaly detection on time series data for multiple state-of-the-art deep learning methods.
Team:
Supervisors:
Malhotra, Pankaj, et al. "Long short term memory networks for anomaly detection in time series." Proceedings. Presses universitaires de Louvain, 2015.
Malhotra, Pankaj, et al. "LSTM-based encoder-decoder for multi-sensor anomaly detection." ICML, 2016.
Hawkins, Simon, et al. "Outlier detection using replicator neural networks." DaWaK, 2002.
Xu, Haowen, et al. "Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications." WWW, 2018.
Zhai, Shuangfei, et al. "Deep structured energy based models for anomaly detection." ICML, 2016.
Zong, Bo, et al. "Deep autoencoding gaussian mixture model for unsupervised anomaly detection." ICLR, 2018.
Extension of Dagmm using an LSTM-Autoencoder instead of a Neural Network Autoencoder
git clone git://github.com/KDD-OpenSource/DeepADoTS.git
virtualenv venv -p /usr/bin/python3
source venv/bin/activate
pip install -r requirements.txt
In the local repository folder, activate a virtual environment first
source venv/bin/activate
python3 main.py
- You can use nvidia-docker
docker build -t deep-adots .
nvidia-docker run -ti deep-adots /bin/bash -c "python3.6 /repo/main.py"
Base implementation for DAGMM
Base implementation for Donut
Base implementation for Recurrent EBM