  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.
