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.
翻译:在半监督环境下广泛研究了多变时间序列异常现象的检测,在半监督环境下需要有一个包含所有正常情况的培训数据集。然而,编制这样一个数据集非常费力,因为每个单一数据实例都应完全保证是正常的。因此,希望探索基于数据集的多变时间序列异常现象检测方法,而没有任何标签知识。在本文件中,我们提议采用MTGFlow,这是一个未经监督的异常现象检测方法,用于通过动态图形和实体自觉正常流来检测多变时间序列异常现象,仅依赖于一种广泛接受的假设,即异常情况比正常情况要少。然而,各实体之间复杂的相互依存性和每个实体的不同固有特征都对密度估算构成重大挑战,更不用说根据估计的可能性分布来检测异常现象。为了解决这些问题,我们提议通过图表结构学习模型来了解各实体之间的相互和动态关系,这有助于模拟多变数时间序列的准确分布。此外,考虑到各个实体的特性,实体自觉正常流比正常密度比正常。 将每个实体的运行流与SOqral-roalal-moal-moal ASimal IM 进行Siral-deal-dealisal-deal-dealmaislational-deal-s-de-slevalislation-deal-deal-dealmamaxxx-deal-deal-deal-smaxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx