论文标题
图表仪的标准化流量以用于多个时间序列的异常检测
Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series
论文作者
论文摘要
对于各种数据类型，异常检测是一项广泛研究的任务。其中，多个时间序列经常出现在应用程序中，包括电源网络和流量网络。但是，由于组成序列之间的复杂相互依赖性，检测多个时间序列的异常是一个具有挑战性的主题。我们假设异常发生在分布的低密度区域中，并探索使用归一化的流量以进行无监管的异常检测，因为它们在密度估计方面具有卓越的质量。此外，我们通过在组成系列中施加贝叶斯网络来提出一种新型的流程模型。贝叶斯网络是建模因果关系的有向无环图（DAG）。它将该系列的联合概率分解为易于评估的条件概率的乘积。我们称之为绘制的归一化流近ganf，并提出了使用流参数对DAG进行关节估计。我们对现实世界数据集进行了广泛的实验，并证明了GANF在密度估计，异常检测和时间序列分布漂移的鉴定方面的有效性。
Anomaly detection is a widely studied task for a broad variety of data types; among them, multiple time series appear frequently in applications, including for example, power grids and traffic networks. Detecting anomalies for multiple time series, however, is a challenging subject, owing to the intricate interdependencies among the constituent series. We hypothesize that anomalies occur in low density regions of a distribution and explore the use of normalizing flows for unsupervised anomaly detection, because of their superior quality in density estimation. Moreover, we propose a novel flow model by imposing a Bayesian network among constituent series. A Bayesian network is a directed acyclic graph (DAG) that models causal relationships; it factorizes the joint probability of the series into the product of easy-to-evaluate conditional probabilities. We call such a graph-augmented normalizing flow approach GANF and propose joint estimation of the DAG with flow parameters. We conduct extensive experiments on real-world datasets and demonstrate the effectiveness of GANF for density estimation, anomaly detection, and identification of time series distribution drift.
