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在多变量时间序列中深度异常检测的调查:分类学,应用和方向
Fengling Wang1, Yiyue Jiang1, Rongjie Zhang2
1School of Artificial Intelligence, South China Normal University, Foshan 528000, China.
Sensors (Basel, Switzerland)
|January 11, 2025
概括
本文概述了用于多变量时间序列异常检测 (MTSAD) 的深度学习技术. 它对方法进行了分类,审查了它们的优缺点,并确定了复杂系统监控的未来研究方向.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 多变量时间序列异常检测 (MTSAD) 对于识别复杂系统中的异常模式至关重要.
- 应用包括财务监控,工业故障检测和网络安全.
- 深度学习模型越来越多地用于分析时间和变量之间的依赖关系.
研究的目的:
- 为MTSAD提供最新的深度学习技术提供结构化和全面的概述.
- 提出基于学习范式和深度学习模型的MTSAD策略的分类法.
- 在MTSAD中确定未解决的问题和未来的研究方向.
主要方法:
- 基于深度学习的MTSAD方法的系统文献综述.
- 开发一个分类系统,对异常检测策略进行分类.
- 编制和组织公共MTSAD数据集及其应用领域.
主要成果:
- 通过学习范式和模型类型对MTSAD方法进行分类的分类学.
- 综述了各种深度学习技术的优缺点.
- 有关MTSAD研究的公共数据集的精选列表.
结论:
- 深度学习为MTSAD提供了强大的工具,但需要明确对方法的分类和理解.
- 需要进一步的研究,以解决多变量时间序列异常检测中的未解决挑战.
- 组织的数据集和确定的研究差距将指导该领域的未来进展.
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