  Detecting anomalies in real-world multivariate time series data is
challenging due to complex temporal dependencies and inter-variable
correlations. Recently, reconstruction-based deep models have been widely used
to solve the problem. However, these methods still suffer from an
over-generalization issue and fail to deliver consistently high performance. To
address this issue, we propose the MEMTO, a memory-guided Transformer using a
reconstruction-based approach. It is designed to incorporate a novel memory
module that can learn the degree to which each memory item should be updated in
response to the input data. To stabilize the training procedure, we use a
two-phase training paradigm which involves using K-means clustering for
initializing memory items. Additionally, we introduce a bi-dimensional
deviation-based detection criterion that calculates anomaly scores considering
both input space and latent space. We evaluate our proposed method on five
real-world datasets from diverse domains, and it achieves an average anomaly
detection F1-score of 95.74%, significantly outperforming the previous
state-of-the-art methods. We also conduct extensive experiments to empirically
validate the effectiveness of our proposed model's key components.
