ALGAN: Time Series Anomaly Detection with Adjusted-LSTM GAN
This addresses anomaly detection for domains like manufacturing and cybersecurity, but appears incremental as it builds on existing GAN-based approaches.
The paper tackles anomaly detection in time series data by proposing ALGAN, a new GAN model that adjusts LSTM outputs, and reports that it outperforms existing methods on 46 univariate and a large multivariate dataset.
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.