jules-samaran/gan-ad

We used generative adversarial networks (GANs) to do anomaly detection for time series data.

-- Multivariate Anomaly Detection for Time Series Data with GANs --

#GAN-AD

This repository contains code for the paper, Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series, by Dan Li, Dacheng Chen, Jonathan Goh, and See-Kiong Ng.

Overview

We used generative adversarial networks (GANs) to do anomaly detection for time series data. The GAN framework was RGAN that taken from the paper, _Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs. Please refer to https://github.com/ratschlab/RGAN for the original code.

Quickstart

• Python3

• Sample generation

  """python RGAN.py --settings_file gp_gen"""

  """python RGAN.py --settings_file sine_gen"""

  """python RGAN.py --settings_file mnistfull_gen"""

  """python RGAN.py --settings_file swat_gen"""

(Please unpack the mnist_train.7z file in the data folder before generate mnist)

• To train the model for anomaly detection:

  """python RGAN.py --settings_file swat_train"""

• To do anomaly detection:

  """python AD.py --settings_file swat_test"""

Data

In this repository, we applied GAN-AD on the SWaT dataset, please refer to https://itrust.sutd.edu.sg/ and send request to iTrust is you want to try the data.