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Due to the scarcity and uncertainty of anomalies, anomaly detection becomes a challenging problem in communities. This paper proposes an effective time series anomaly detection network based on prediction and adversarial training. First, we employ LSTM model based on differential attention mechanism to fully extract the inner characters of time series. To prevent the over-fitting due to only normal samples used to train our LSTM model, we introduce an adversarial training strategy by adding adversarial samples to enhance the generalization of the model on normal data. The effectiveness of our model is demonstrated on several public datasets.

An Effective Adversarial Training and Prediction Network for Time Series Anomaly Detection