What Is A Time Series GAN?

Identifying anomalies in time series data can be daunting, thanks to the vague definition of anomalies, lack of labelled data, and highly complex temporal correlations. Currently, the machine learning method used for anomaly detection faces scalability and portability issues, resulting in false-positives. And, therein lies the problem. In a recent paper, MIT researchers proposed an unsupervised anomaly detection approach — TadGAN, that allows time series reconstruction and effectively flag anomalies in the data. Built on generative adversarial networks (GANs), Time Series GAN or TadGAN has been trained with cycle consistency loss to allow for effective time-series data reconstruction. With a claim to outperform baseline methods in most cases, the researchers are planning to present