Meta Variational Memory Transformer for Anomaly Detection of Multivariate Time Series.

Detecting anomalies in multivariate time series (MTS) is a crucial task in areas like financial fraud detection and industrial equipment monitoring. Recent research has focused on developing unsupervised probabilistic models to identify anomalous patterns within MTS. However, many of these methods rely on fixed parameter mappings for each MTS, resulting in high computational costs and limited adaptability. To overcome these challenges, we introduce a novel Meta Variational Memory Transformer (MVMT). MVMT captures the diverse patterns across various MTS by encoding them into a set of memory units using a specially developed meta memory attention (MMA) module. Utilizing these learned memory units, we introduce a memory-guided probabilistic generative model that selects relevant memories as priors for latent states, resulting in more expressive MTS representations. A key feature of MVMT is that MMA provides a diversified prior in the latent space, ensuring the generation of various patterns. Finally, we implement a Transformer-based upward-downward variational inference process to estimate the posterior distribution of latent variables. Our extensive experiments on six datasets demonstrate the effectiveness of MVMT in one-for-all anomaly detection tasks.