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：深度学习技术在时间序列数据异常检测中的应用是一个长期活跃的研究领域，并发展出多种方法。其中，基于重构的无监督异常检测方法因其直观的假设和较低的计算需求而广受欢迎。然而，这些方法通常对异常值敏感，且无法有效建模异常，导致结果欠佳。本文提出一种名为协同网络时间序列（CNTS）的无监督异常检测新方法。CNTS系统由两个组件构成：检测器与重构器。检测器负责直接识别异常，而重构器则为检测器提供重构信息，并根据从检测器接收的异常信息更新自身学习过程。CNTS的核心在于一个多目标优化问题，该问题通过协同求解策略加以解决。在三个真实数据集上的实验表明，CNTS达到了最先进的性能，并验证了检测器与重构器之间的协同有效性。本研究的源代码已在GitHub上公开。