The use of deep learning techniques in detecting anomalies in time series data has been an active area of research with a long history of development and a variety of approaches. In particular, reconstruction-based unsupervised anomaly detection methods have gained popularity due to their intuitive assumptions and low computational requirements. However, these methods are often susceptible to outliers and do not effectively model anomalies, leading to suboptimal results. This paper presents a novel approach for unsupervised anomaly detection, called the Cooperative Network Time Series (CNTS) approach. The CNTS system consists of two components: a detector and a reconstructor. The detector is responsible for directly detecting anomalies, while the reconstructor provides reconstruction information to the detector and updates its learning based on anomalous information received from the detector. The central aspect of CNTS is a multi-objective optimization problem, which is solved through a cooperative solution strategy. Experiments on three real-world datasets demonstrate the state-of-the-art performance of CNTS and confirm the cooperative effectiveness of the detector and reconstructor. The source code for this study is publicly available on GitHub.
翻译:利用深层次学习技术探测时间序列数据中的异常现象是一个积极的研究领域,其发展历史悠久,采用的方法多种多样,特别是,重建型未经监督的异常现象探测方法由于其直觉假设和低计算要求而越来越受欢迎,然而,这些方法往往易受外部线的影响,不能有效地模拟异常现象,结果不尽人意。本文件介绍了一种新颖的方法,用于在不受监督的情况下探测异常现象,称为合作网络时间序列(CNTS)方法。CNTS系统由两个部分组成:探测器和再生器。探测器负责直接探测异常现象,而重建型探测器则根据从探测器收到的异常信息向探测器提供重建信息并更新其学习情况。国家技术研究中心的中心是一个多目标优化问题,通过合作解决方案战略加以解决。对三个真实世界数据集的实验表明CNTS的现状,并证实探测器和再生器的合作效能。这项研究的源代码在GitHub公开提供。