VELC: A New Variational AutoEncoder Based Model for Time Series Anomaly Detection

Understand

Anomaly detection is a classical but worthwhile problem, and many deep learning-based anomaly detection algorithms have been proposed, which can usually achieve better detection results than traditional methods.

• In view of reconstruct ability of the model and the calculation of anomaly score, this paper proposes a time series anomaly detection method based on Variational AutoEncoder model(VAE) with re-Encoder and Latent Constraint network(VELC).
• In order to modify reconstruct ability of the model to prevent it from reconstructing abnormal samples well, we add a constraint network in the latent space of the VAE to force it generate new latent variables that are similar with that of training samples.
• To be able to calculate anomaly score in two feature spaces, we train a re-encoder to transform the generated data to a new latent space.