  Anomaly detection in time series data, to identify points that deviate from
normal behaviour, is a common problem in various domains such as manufacturing,
medical imaging, and cybersecurity. Recently, Generative Adversarial Networks
(GANs) are shown to be effective in detecting anomalies in time series data.
The neural network architecture of GANs (i.e. Generator and Discriminator) can
significantly improve anomaly detection accuracy. In this paper, we propose a
new GAN model, named Adjusted-LSTM GAN (ALGAN), which adjusts the output of an
LSTM network for improved anomaly detection in both univariate and multivariate
time series data in an unsupervised setting. We evaluate the performance of
ALGAN on 46 real-world univariate time series datasets and a large multivariate
dataset that spans multiple domains. Our experiments demonstrate that ALGAN
outperforms traditional, neural network-based, and other GAN-based methods for
anomaly detection in time series data.
