基于聚类的多变量时间序列数据异常检测
Clustering-based Anomaly Detection in Multivariate Time Series Data
AI总结：
针对多变量时间序列异常检测需同时考虑时间与变量关系的问题，提出基于聚类的方法，通过滑动窗口生成子序列、扩展模糊聚类揭示结构、重构准则与置信指数检测异常，实验验证其在多领域数据集上的有效性。
摘要：
多变量时间序列数据是描述某一时间现象不同方面的时间序列集合。这类数据的异常检测是一个具有挑战性的问题，但在科学和工程领域有众多应用，因为异常评分需要同时考虑时间关系和变量关系。本文提出一种基于聚类的方法来检测多变量时间序列在幅度和形状方面的异常。首先，我们使用滑动窗口生成一组多变量子序列，然后应用扩展模糊聚类来揭示生成的多变量子序列中存在的结构。最后，采用重构准则，利用最优聚类中心和划分矩阵重构多变量子序列。我们构建置信指数来量化序列中检测到的异常水平，并将粒子群优化作为异常检测问题的优化工具。在多个合成数据集和6个真实世界数据集上完成的实验研究表明，所提出的方法能够检测多变量时间序列中的异常。借助扩展模糊聚类揭示的可用聚类，所提出的框架可以检测多变量时间序列中的异常，适用于识别医疗保健、天气数据分析、金融和疾病爆发检测等各种应用领域中的异常幅度和形状模式。
英文摘要：
Multivariate time series data come as a collection of time series describing different aspects of a certain temporal phenomenon. Anomaly detection in this type of data constitutes a challenging problem yet with numerous applications in science and engineering because anomaly scores come from the simultaneous consideration of the temporal and variable relationships. In this paper, we propose a clustering-based approach to detect anomalies concerning the amplitude and the shape of multivariate time series. First, we use a sliding window to generate a set of multivariate subsequences and thereafter apply an extended fuzzy clustering to reveal a structure present within the generated multivariate subsequences. Finally, a reconstruction criterion is employed to reconstruct the multivariate subsequences with the optimal cluster centers and the partition matrix. We construct a confidence index to quantify a level of anomaly detected in the series and apply Particle Swarm Optimization as an optimization vehicle for the problem of anomaly detection. Experimental studies completed on several synthetic and six real-world datasets suggest that the proposed methods can detect the anomalies in multivariate time series. With the help of available clusters revealed by the extended fuzzy clustering, the proposed framework can detect anomalies in the multivariate time series and is suitable for identifying anomalous amplitude and shape patterns in various application domains such as health care, weather data analysis, finance, and disease outbreak detection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
