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The loss function of Generative adversarial network(GAN) is an importantfactor that affects the quality and diversity of the generated samples foranomaly detection. In this paper, we propose an unsupervised multiple timeseries anomaly detection algorithm based on the GAN with message importancemeasure(MIM-GAN). In particular, the time series data is divided intosubsequences using a sliding window. Then a generator and a discriminatordesigned based on the Long Short-Term Memory (LSTM) are employed to capture thetemporal correlations of the time series data. To avoid the local optimalsolution of loss function and the model collapse, we introduce an exponentialinformation measure into the loss function of GAN. Additionally, a discriminantreconstruction score consisting on discrimination and reconstruction loss istaken into account. The global optimal solution for the loss function isderived and the model collapse is proved to be avoided in our proposedMIM-GAN-based anomaly detection algorithm. Experimental results show that theproposed MIM-GAN-based anomaly detection algorithm has superior performance interms of precision, recall, and F1 score.

MIM-GAN-based Anomaly Detection for Multivariate Time Series Data