Anomaly detectionTo ensure the stable long-time operation of satellites, evaluate the satellite status, and improve satellite maintenance efficiency, we propose an anomaly detection method based on graph neural network and dynamic threshold (GNN-DTAN). Firstly, we build the graph neural network model...
一是不同传感器之间有着非常不同的行为,即图中节点的数值和分布差异很大,因此需要考虑如何对传感器,即图中节点进行特征表示;二是GNNs的输入必须是整个图,即包括图中节点的特征表示以及各节点的连接关系,而在本文场景中,各节点之间的关系都是未知的(以往的方法是直接认为各节点之间都存在关系,即使用完全图表征各节点...
回顾了异常检测(Anomaly Detection)、多元时间序列数据模型 (models for multivariate time series data)、图神经网络(Graph neural network)的研究相关工作,并指出其不足。 2.1 异常检测(Anomaly Detection) 目的是检测出偏离大部分数据的异常样本,经典方法包括基于密度的研究方法、基于线性模型的研究方法、基于距离的研究...
Xiao et al. [11] proposed a graph embedding approach to perform anomaly detection on network flows. The authors first converted the network flows into a first-order and secondorder graph. The first-order graph learns the latent features from the perspective of a single host by using its IP ...
Multivariate Time Series Anomaly Detection Using Graph Neural Network This example uses: Deep Learning Toolbox Statistics and Machine Learning Toolbox Copy Code Copy CommandThis example shows how to detect anomalies in multivariate time series data using a graph neural network (GNN)....
Code implementation for :Graph Neural Network-Based Anomaly Detection in Multivariate Time Series(AAAI'21) Installation Python >= 3.6 cuda == 10.2 Pytorch==1.5.1 PyG: torch-geometric==1.5.0 Install packages # run after installing correct Pytorch package bash install.sh ...
For industrial big data, anomaly detection for multivariate time series data is of critical strategic significance. However, the complexity of industrial e
Xiao et al. [11] proposed a graph embedding approach to perform anomaly detection on network flows. The authors first converted the network flows into a first-order and secondorder graph. The first-order graph learns the latent features from the perspective of a single host by using its IP ...
Motivated by the demand for wind turbines anomaly detection, the stronger representation capability of graph data, and the fast development of unsupervised graph neural networks, this work presents an anomaly detection framework for wind turbines using SCADA data based on physical-statistical feature fusi...
论文链接:[2106.06947v1] Graph Neural Network-Based Anomaly Detection in Multivariate Time Series (arxiv.org) 主要内容 论文提出了一种图偏差网络(GDN)框架用于多元时间序列异常检测,该框架可以实验对一个系统中各个传感器之间的结构关系的建模,传感器异常检测以及异常传感器的定位等,通过两个水处理厂的数据集进行...