MIM-GAN-based Anomaly Detection for Multivariate Time Series Data
Shan Lu Zhicheng Dong Donghong Cai Fang Fang Dongcai Zhao
The loss function of Generative adversarial network(GAN) is an important
factor that affects the quality and diversity of the generated samples for
anomaly detection. In this paper, we propose an unsupervised multiple time
series anomaly detection algorithm based on the GAN with message importance
measure(MIM-GAN). In particular, the time series data is divided into
subsequences using a sliding window. Then a generator and a discriminator
designed based on the Long Short-Term Memory (LSTM) are employed to capture the
temporal correlations of the time series data. To avoid the local optimal
solution of loss function and the model collapse, we introduce an exponential
information measure into the loss function of GAN. Additionally, a discriminant
reconstruction score consisting on discrimination and reconstruction loss is
taken into account. The global optimal solution for the loss function is
derived and the model collapse is proved to be avoided in our proposed
MIM-GAN-based anomaly detection algorithm. Experimental results show that the
proposed MIM-GAN-based anomaly detection algorithm has superior performance in
terms of precision, recall, and F1 score.