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MFAM-AD:用于多变量时间序列的异常检测模型,使用注意力机制来融合多尺度特征

Shengjie Xia¹, Wu Sun¹, Xiaofeng Zou¹

¹State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, China.

PeerJ. Computer science

|September 24, 2024

概括

通过整合多尺度特征和时间依赖,MFAM-AD模型有效地检测多变量时间序列数据中的异常. 这种方法提高了准确性,在几个基准数据集上表现优于现有方法.

科学领域:

数据科学数据科学数据科学
机器学习机器学习
人工智能的人工智能

背景情况:

多变量时间序列异常检测在IT,金融和医学等各个领域至关重要.
现有的模型难以捕捉异常的多尺度时间变化,限制检测准确度.
需要先进的模型,可以有效地整合空间和时间特征,以进行强大的异常检测.

研究的目的:

为增强多变量时间序列异常检测提出MFAM-AD模型.
解决现有模型在捕捉多尺度时间变化和特征融合方面的局限性.
提高复杂时间序列数据中异常检测的准确性和有效性.

主要方法:

MFAM-AD模型结合了卷积神经网络 (CNN) 来进行多尺度特征提取和双向长期短期记忆 (Bi-LSTM) 来进行时间依赖模型.
平行卷积层提取不同尺度的特征,具有最佳融合的注意力机制.
Bi-LSTM捕获时间依赖的信息,重建时间序列,并根据重建错误识别异常.

主要成果:

在五个公共数据集上的实验结果表明了MFAM-AD模型的优越性.
在SMAP,MSL和SMD1-1数据集上,MFAM-AD获得了第二高的F1得分,紧随其后的是最先进的DCdetector.
在NIPS-TS-SWAN和NIPS-TS-GECCO数据集中,MFAM-AD显示F1得分显著改善 (分别高出6.2%和21.3%),与DCdetector相比.

结论:

MFAM-AD模型通过整合多尺度的空间和时间特征,有效地解决了多变量时间序列异常检测的挑战.
该模型的性能验证了其有效性,并突出了其在需要精确异常检测的现实应用中的潜力.
与现有的算法相比,MFAM-AD提供了一个有前途的进步,特别是在具有复杂,多尺度异常模式的场景中.

关键词:

异常检测检测异常检测注意力机制注意力机制双向的长期短期记忆.卷积神经网络是一种卷积神经网络.多个尺度的多个尺度.

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