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
Detecting credit card fraud in real-time has become increasingly important with the rise of electronic transactions. However, existing methods often fail to deliver high accuracy and robustness due to class imbalance and overlapping features. To address these issues, Anomaly Detection in Online Credit ...
· 一个基于 C# 编写的事件驱动、具备专业水准的算法交易平台(量化交易引擎) · VS Code + Cline + 魔搭MCP Server 实现抓取网页内容。 · 好端端的线程池,怎么就卡死了? Graph Neural Networks based Log Anomaly Detection and Explanation论文阅读笔记 2024-06-29 17:18660040638:07 ~ 13:32 MENU 博客...
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...
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)....
Keywords: anomaly detection; financial time series; principal component analysis; neural network; missing data; market risk; value at risk1. Introduction In the context of financial risk management, financial risk models are of utmost importance in order to quantify and manage financial risk. Their ...
Frequency Self-Adaptation Graph Neural Network for Unsupervised Graph Anomaly Detection Ming Gu, ... Jiajun Bu October 2025 More from Neural Networks 31 August 2022 Fostering deep learning and beyond 31 August 2022 Calls for papers Model Compression in the Era of Large Language Models ...
we propose one-class graph neural network (OCGNN), a one-class classification framework for graph anomaly detection. OCGNN is designed to combine the powerful representation ability of graph neural networks along with the classical one-class objective. Compared with other baselines, OCGNN achieves ...
Making traditional classification CNNs to work as a fully convolutional network and using a regional feature extractor reduces computation costs. In general, as CNNs or FCNs are supervised methods, neither CNNs nor FCNs are capable for solving anomaly detection tasks, To overcome aforementioned ...