用于多变量时间序列异常检测的元变量记忆变压器
Kun Qin1,2, Yuxin Li2, Wenchao Chen2
1Shanghai Aerospace Electronic Communication Equipment Institute, Shanghai 201109, China.
Sensors (Basel, Switzerland)
|December 31, 2025
概括
我们开发了一种新的元变量记忆转换器 (MVMT),用于在多变量时间序列 (MTS) 中无监督检测异常. MVMT有效地捕捉了各种模式,提高了适应性,并降低了欺诈检测等任务的计算成本.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 时间序列分析时间序列分析
背景情况:
- 在多变量时间序列 (MTS) 中检测异常对于金融欺诈和设备监控至关重要.
- 目前用于MTS异常检测的无监督概率模型面临着由于固定的参数映射而导致高计算成本和有限的适应能力的挑战.
- 对于MTS异常检测,需要更具适应性和计算效率的方法.
研究的目的:
- 引入一种新型的元变量记忆转换器 (MVMT),用于在MTS中无监督检测异常.
- 提高现有的MTS异常检测技术的适应性和减少计算负担.
- 开发一种能够有效地捕捉各种MTS中的多样化模式的模型.
主要方法:
- 开发了一个元变量记忆转换器 (MVMT),结合了一个元记忆注意 (MMA) 模块,将各种MTS模式编码到记忆单元中.
- 介绍了一种记忆引导的概率生成模型,该模型使用学习记忆单元作为潜在状态的先验,创建表达式的MTS表示.
- 实现了基于变压器的上下波动推理,用于估计隐性变量后部分布.
主要成果:
- 拟议的MVMT模型在六个不同的数据集中证明了对一对一的异常检测任务的有效性.
- 该MMA模块通过在潜伏空间中提供多样化的前置来实现各种模式的生成.
- 通过内存引导的隐藏状态先验,MVMT实现了更具表现力的MTS表示.
结论:
- MVMT在多变量时间序列的无监督异常检测方面取得了重大进展.
- 与以前的方法相比,该模型的架构提高了适应性和效率.
- 在欺诈检测和工业监控方面,MVMT显示出强大的现实应用潜力.
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