Weakly Augmented Variational Autoencoder in Time Series Anomaly
  Detection
Zhangkai Wu Longbing Cao Qi Zhang Junxian Zhou Hui Chen
Due to their unsupervised training and uncertainty estimation, deep
Variational Autoencoders (VAEs) have become powerful tools for
reconstruction-based Time Series Anomaly Detection (TSAD). Existing VAE-based
TSAD methods, either statistical or deep, tune meta-priors to estimate the
likelihood probability for effectively capturing spatiotemporal dependencies in
the data. However, these methods confront the challenge of inherent data
scarcity, which is often the case in anomaly detection tasks. Such scarcity
easily leads to latent holes, discontinuous regions in latent space, resulting
in non-robust reconstructions on these discontinuous spaces. We propose a novel
generative framework that combines VAEs with self-supervised learning (SSL) to
address this issue.