论文标题
通过图神经网络检测无监督的更改点检测
Correlation-aware Unsupervised Change-point Detection via Graph Neural Networks
论文作者
论文摘要
变更点检测（CPD）旨在检测超时数据序列数据的突然变化。直觉上，多元时间序列上的有效CPD应需要对跨输入变量的依赖项进行明确建模。但是，现有的CPD方法要么完全忽略依赖性结构，要么依赖于（不现实的）相关结构随时间静态的（不现实的）假设。在本文中，我们为CPD提出了一个相关感知的动力学模型，该模型通过将图形神经网络纳入编码器删除框架中明确对变量的相关结构和动力学进行了建模。关于合成和现实世界数据集的广泛实验证明了拟议模型对CPD任务的优势性能，而不是强大的基线，以及将变更点分类为相关性变化或独立更改的能力。关键字：多元时间序列，更改点检测，图形神经网络
Change-point detection (CPD) aims to detect abrupt changes over time series data. Intuitively, effective CPD over multivariate time series should require explicit modeling of the dependencies across input variables. However, existing CPD methods either ignore the dependency structures entirely or rely on the (unrealistic) assumption that the correlation structures are static over time. In this paper, we propose a Correlation-aware Dynamics Model for CPD, which explicitly models the correlation structure and dynamics of variables by incorporating graph neural networks into an encoder-decoder framework. Extensive experiments on synthetic and real-world datasets demonstrate the advantageous performance of the proposed model on CPD tasks over strong baselines, as well as its ability to classify the change-points as correlation changes or independent changes. Keywords: Multivariate Time Series, Change-point Detection, Graph Neural Networks
