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README.md

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-# Multivariate Anomaly Detection for Time Series Data with GANs
+# -- Multivariate Anomaly Detection for Time Series Data with GANs -- #
+
+# MAD-GAN
+
+This repository contains code for the paper, _[Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series](https://arxiv.org/pdf/1809.04758.pdf)_, 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 **R**GAN that taken from the paper, _[Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs](https://arxiv.org/abs/1706.02633).
+Please refer to https://github.com/ratschlab/RGAN for the original code.
+
+## Quickstart
+
+- Python3
+
+- To train the model:
+  $ cd MAD-GAN-master
+  $ python RGAN.py --settings_file kdd99
+
+- To do anomaly detection:
+  $ cd MAD-GAN-master
+  $ python AD.py --settings_file kdd99_test
+  $ python AD_Invert.py --settings_file kdd99_test
+
+## Data
+
+We apply our method on the SWaT and WADI datasets in the paper, however, we didn't upload the data in this repository. Please refer to https://itrust.sutd.edu.sg/ and send request to iTrust is you want to try the data.
+
+In this repository we used kdd cup 1999 dataset as an example. You can also down load the original data at http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html
+