无标签多元时间序列异常检测
Label-Free Multivariate Time Series Anomaly Detection
AI总结：
针对无标签多元时间序列异常检测，提出MTGFlow方法，利用动态图和实体感知归一化流估计密度，并扩展聚类策略MTGFlow_cluster，在六个基准数据集上取得优越性能。
摘要：
多元时间序列（MTS）中的异常检测已在单类分类（OCC）设置下被广泛研究。OCC中的训练样本被假定为正常的，但在实际情况下这一假设难以保证。这种情况可能会降低基于OCC的异常检测方法的性能，因为这些方法将训练分布拟合为正常分布。在本文中，我们提出了MTGFlow，一种通过动态图和实体感知归一化流进行MTS异常检测的无监督异常检测方法。MTGFlow首先估计整个训练样本的密度，然后根据测试样本在拟合分布中的密度来识别异常实例。这依赖于一个广泛接受的假设，即异常实例比正常实例表现出更稀疏的密度，且不依赖于干净的训练数据集。然而，由于实体之间复杂的依赖关系及其多样化的固有特征，直接估计密度是困难的。为了缓解这一问题，我们利用图结构学习模型来学习实体之间相互依赖且不断演化的关系，从而有效捕获MTS复杂而准确的分布模式。此外，我们的方法通过采用实体感知归一化流来纳入个体实体的独特特征。这使我们能够将每个实体表示为一个参数化的正态分布。进一步地，考虑到一些实体呈现相似的特征，我们提出了一种聚类策略，利用具有相似特征的实体的共性，从而实现更精确和详细的密度估计。我们将这种聚类感知的扩展称为MTGFlow_cluster。在六个广泛使用的基准数据集上进行了大量实验，其中MTGFlow和MTGFlow_cluster展示了其优越的检测性能。
英文摘要：
Anomaly detection in multivariate time series (MTS) has been widely studied in one-class classification (OCC) setting. The training samples in OCC are assumed to be normal, which is difficult to guarantee in practical situations. Such a case may degrade the performance of OCC-based anomaly detection methods which fit the training distribution as the normal distribution. In this paper, we propose MTGFlow, an unsupervised anomaly detection approach for MTS anomaly detection via dynamic Graph and entity-aware normalizing Flow. MTGFlow first estimates the density of the entire training samples and then identifies anomalous instances based on the density of the test samples within the fitted distribution. This relies on a widely accepted assumption that anomalous instances exhibit more sparse densities than normal ones, with no reliance on the clean training dataset. However, it is intractable to directly estimate the density due to complex dependencies among entities and their diverse inherent characteristics. To mitigate this, we utilize the graph structure learning model to learn interdependent and evolving relations among entities, which effectively captures complex and accurate distribution patterns of MTS. In addition, our approach incorporates the unique characteristics of individual entities by employing an entity-aware normalizing flow. This enables us to represent each entity as a parameterized normal distribution. Furthermore, considering that some entities present similar characteristics, we propose a cluster strategy that capitalizes on the commonalities of entities with similar characteristics, resulting in more precise and detailed density estimation. We refer to this cluster-aware extension as MTGFlow_cluster. Extensive experiments are conducted on six widely used benchmark datasets, in which MTGFlow and MTGFlow cluster demonstrate their superior detection performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
