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.

翻译：深度变分自编码器（VAEs）凭借其无监督训练和不确定性估计能力，已成为基于重构的时间序列异常检测（TSAD）的有力工具。现有的基于VAE的时间序列异常检测方法，无论是统计方法还是深度方法，均通过调节元先验来估计似然概率，从而有效捕捉数据中的时空依赖关系。然而，这些方法面临异常检测任务中常见的数据稀缺性挑战。这种稀缺性容易导致潜在空间出现空洞和不连续区域，进而造成模型在这些不连续空间上的重构缺乏鲁棒性。为解决此问题，我们提出了一种融合VAE与自监督学习（SSL）的新型生成框架。