Scholarlink

<p>Graph neural networks (GNN) for spatiotemporal problems incorporate a temporal module operating on a temporal grid of interconnected subsequent time steps. In this work, we explore adding connections to future time steps to the temporal grid in order to extract unique features for the task of anomaly detection in time series. We introduce strategies for determining future connections utilizing the autocorrelation function and different random sampling techniques. The effectiveness of resulting graph neural networks is demonstrated on multiple anomaly detection benchmarks, including spacecraft telemetry datasets. A selection of graph neural network models is further deployed on the AMD-Xilinx Versal AI Core SoC to measure the execution time and resource utilization when processing telemetry data.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Augmented temporal grid for graph neural networks and anomaly detection in spacecraft

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月1日

查看全文
  • Gamze Naz Kiprit,
  • Andreas Koch,
  • Michael Petry,
  • Martin Werner

摘要

Graph neural networks (GNN) for spatiotemporal problems incorporate a temporal module operating on a temporal grid of interconnected subsequent time steps. In this work, we explore adding connections to future time steps to the temporal grid in order to extract unique features for the task of anomaly detection in time series. We introduce strategies for determining future connections utilizing the autocorrelation function and different random sampling techniques. The effectiveness of resulting graph neural networks is demonstrated on multiple anomaly detection benchmarks, including spacecraft telemetry datasets. A selection of graph neural network models is further deployed on the AMD-Xilinx Versal AI Core SoC to measure the execution time and resource utilization when processing telemetry data.