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蒙面图形神经网络用于在多变量时间序列中无监督检测异常
Kang Xu1,2, Yuan Li1, Yixuan Li3
1School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.
Sensors (Basel, Switzerland)
|September 9, 2023
概括
蒙面图形神经网络 (MGUAD) 通过学习传感器因果关系来增强无监督异常检测. 这种新的方法有效地识别高维,多变量时间序列数据中的异常,优于现有方法.
科学领域:
- 数据科学数据科学数据科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 多变量时间序列异常检测对于电网运行和机器故障检测等应用至关重要.
- 具有潜伏关系的高维传感器数据对现有的无监督方法构成重大挑战.
- 目前的方法很难捕捉复杂的传感器间关联,并有效地利用潜在的关系.
研究的目的:
- 为多变量时间序列数据提出一种新的无监督异常检测方法.
- 为了解决捕获复杂传感器相关性的现有方法的局限性.
- 开发一种能够学习传感器之间未被观察到的因果结构的模型.
主要方法:
- 介绍了用于无监督异常检测 (MGUAD) 的掩盖图形神经网络.
- 使用掩盖和重建策略,从单个传感器数据中学习时间上下文.
- 在GNN框架内利用图形级别的掩盖来学习传感器之间的关系.
主要成果:
- 在三个公共数据集中,MGUAD在异常检测方面表现出卓越的表现.
- 该方法有效地学习传感器之间的未观察到的因果结构.
- 实验发现表明,MGUAD的性能优于最先进的异常检测技术.
结论:
- MGUAD提供了一个强大的解决方案,用于在高维多变量时间序列中无监督检测异常.
- 拟议的掩盖方法增强了时间和图表层次上下文的学习.
- 这种方法通过更好地利用传感器数据中的潜在关系来推动该领域的进步.
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