Skip to content
master
Switch branches/tags
Go to file
Code

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

-- 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.

About

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

Resources

Releases

No releases published

Packages

No packages published

Languages