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Found 2 papers, 2 papers with code
MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks
The prevalence of networked sensors and actuators in many real-world systems such as smart buildings, factories, power plants, and data centers generate substantial amounts of multivariate time series data for these systems.
Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series
We used LSTM-RNN in our GAN to capture the distribution of the multivariate time series of the sensors and actuators under normal working conditions of a CPS.
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