在时间序列数据中检测异常使用可逆实例规范异常变压器
1Kpro System, 673-1 Dogok-ri, Wabu-eup, Namyangju-si 12270, Gyeonggi-do, Republic of Korea.
Sensors (Basel, Switzerland)
|November 25, 2023
概括
本研究介绍了可逆实例规范异常变压器 (RINAT) 用于异常检测. 通过分析正常化和非正常化数据关联,RINAT有效地识别了罕见的异常.
科学领域:
- 数据科学数据科学数据科学
- 机器学习 机器学习
- 时间序列分析时间序列分析
背景情况:
- 异常检测对系统完整性至关重要,但由于异常的不频繁性质,具有挑战性.
- 现有的模型通常依赖于相邻数据点之间的关系,这对于罕见事件可能是不够的.
研究的目的:
- 开发一种新的异常检测方法,利用异常的稀有性.
- 介绍可逆实例规范异常变压器 (RINAT) 模型.
主要方法:
- 瑞纳特是建立在异常变压器的基础上,结合了先前和序列协会.
- 优先关联使用可学习的高斯内核来实现归纳偏差,而序列关联则在原始数据上采用自我注意.
- 规范化数据用于序列关联,而非规范化数据用于先前的关联,以增强检测.
主要成果:
- 拟议的方法通过战略性地使用规范化和非规范化数据,有效地增强模拟序列的关联.
- 这种方法可以更好地检测关联差异,这对于识别异常至关重要.
结论:
- 通过独特地利用数据规范化策略,RINAT提供了一种强大的异常检测方法.
- 该模型能够捕获本地和全球数据模式,从而提高了罕见异常的识别能力.
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