  Multivariate time series anomaly detection has been extensively studied under
the semi-supervised setting, where a training dataset with all normal instances
is required. However, preparing such a dataset is very laborious since each
single data instance should be fully guaranteed to be normal. It is, therefore,
desired to explore multivariate time series anomaly detection methods based on
the dataset without any label knowledge. In this paper, we propose MTGFlow, an
unsupervised anomaly detection approach for multivariate time series anomaly
detection via dynamic graph and entity-aware normalizing flow, leaning only on
a widely accepted hypothesis that abnormal instances exhibit sparse densities
than the normal. However, the complex interdependencies among entities and the
diverse inherent characteristics of each entity pose significant challenges on
the density estimation, let alone to detect anomalies based on the estimated
possibility distribution. To tackle these problems, we propose to learn the
mutual and dynamic relations among entities via a graph structure learning
model, which helps to model accurate distribution of multivariate time series.
Moreover, taking account of distinct characteristics of the individual
entities, an entity-aware normalizing flow is developed to describe each entity
into a parameterized normal distribution, thereby producing fine-grained
density estimation. Incorporating these two strategies, MTGFlow achieves
superior anomaly detection performance. Experiments on five public datasets
with seven baselines are conducted, MTGFlow outperforms the SOTA methods by up
to 5.0 AUROC\%. Codes will be released at
https://github.com/zqhang/Detecting-Multivariate-Time-Series-Anomalies-with-Zero-Known-Label.
