Time series data anomaly detection based on LSTM-GAN

Abstract

With the improvement of modern technology, a large number of time series data have been produced. The anomaly detection of time series data can provide relevant information for key situations faced by various fields. This paper proposed an unsupervised temporal anomaly detection method based on generation countermeasure network. In this model, Wasserstein distance is used instead of the original measurement method, and LSTM is used as the basic network of GAN. The model uses the reconstruction loss of the generator and the loss of the discriminator to define the anomaly function to judge the anomaly. This paper uses real world time series data sets involving various fields to evaluate the model. Experiments show that the model is effective in anomaly detection of time series data.