Time Series Anomaly Detection Based on Self-Masked Reconstruction Error With E-AVAE

Anomaly detection of time series has been widely used in many practical domains, such as network security, industrial inspection, temperature monitoring. However, because the observed values of time series are disturbed by a large amount of noise to be nonlinear and non-stationary, traditional anomaly detection methods are unable to achieve superior effect. To tackle this problem, considering the influence on the reconstruction of different type time series, we propose an empirical mode decomposition (EMD) plus Attention Variational Auto-Encoder (E-AVAE) model including three parts to realize time series anomaly detection based on self-masked reconstruction error. In the first part, EMD is used to decompose the original time series to obtain Intrinsic Mode Functions (IMFs) and residual function. The second part is to obtain the set of reconstruction error using AVAE model which combines self-attention mechanism, VAE and Long Short-Term Memory (LSTM). Based on density peak clustering, a self-masked reconstruction error iterative clustering algorithm is proposed to detect outliers of abnormal time series in the last part. Experiments on four real time series datasets show that the E-AVAE model has outstanding performance in accuracy and model interpretation.