将多变量时间序列转为单变量，用隐马尔可夫模型检测异常。
Multivariate Time series Anomaly Detection:A Framework of Hidden Markov Models
- 通过模糊聚类与积分将多变量转为单变量信号。
- 基于隐马尔可夫模型实现异常检测，效果优于传统方法。
- 适合处理复杂工业传感器数据中的异常识别任务。
本研究提出一种多变量时间序列异常检测方法，核心思想是将多变量时间序列转化为单变量时间序列。研究探讨了多种转换技术，包括模糊C均值（FCM）聚类和模糊积分。在此基础上，采用常见的统计方法——隐马尔可夫模型（HMM），构建基于HMM的异常检测器，并对不同转换方法进行了比较分析。实验结果表明，该框架在多个真实数据集上表现良好，能有效识别异常模式。研究还提供了全面的对比分析，验证了所提方法的可行性与有效性。
原文摘要 · Abstract (English)
In this study, we develop an approach to multivariate time series anomaly detection focused on the transformation of multivariate time series to univariate time series. Several transformation techniques involving Fuzzy C-Means (FCM) clustering and fuzzy integral are studied. In the sequel, a Hidden Markov Model (HMM), one of the commonly encountered statistical methods, is engaged here to detect anomalies in multivariate time series. We construct HMM-based anomaly detectors and in this context compare several transformation methods. A suite of experimental studies along with some comparative analysis is reported.
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