DeepAI AI Chat
Log In Sign Up

CNTS: Cooperative Network for Time Series

by   JinSheng Yang, et al.
NetEase, Inc

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.


page 1

page 4

page 5

page 7

page 9

page 10


Time-Series Anomaly Detection with Implicit Neural Representation

Detecting anomalies in multivariate time-series data is essential in man...

TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks

Time series anomalies can offer information relevant to critical situati...

Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time series

Detecting anomalies in time series data is important in a variety of fie...

RLAD: Time Series Anomaly Detection through Reinforcement Learning and Active Learning

We introduce a new semi-supervised, time series anomaly detection algori...

A Robust and Explainable Data-Driven Anomaly Detection Approach For Power Electronics

Timely and accurate detection of anomalies in power electronics is becom...

Anomaly Detection for Fraud in Cryptocurrency Time Series

Since the inception of Bitcoin in 2009, the market of cryptocurrencies h...

Multi-Level Anomaly Detection on Time-Varying Graph Data

This work presents a novel modeling and analysis framework for graph seq...