通过变量分组提升多变量时间序列异常检测精度
Anomaly detection by partitioning of multi-variate time series
- 按变量间相关性聚类，分组进行局部异常检测
- 在合成与真实数据集上显著提升检测效果
- 适合处理高维时序数据的异常发现任务
本文提出一种新型无监督分区异常检测方法PARADISE，用于多变量时间序列异常检测。该方法在保持变量间关系不变的前提下，基于变量间多重相关系数的聚类，将变量划分为若干子集，随后对每个子集独立执行异常检测算法。通过在多个合成数据集和来自文献的真实数据集上进行实验，验证了该方法的有效性，显著提升了异常检测性能。
原文摘要 · Abstract (English)
In this article, we suggest a novel non-supervised partition based anomaly detection method for anomaly detection in multivariate time series called PARADISE. This methodology creates a partition of the variables of the time series while ensuring that the inter-variable relations remain untouched. This partitioning relies on the clustering of multiple correlation coefficients between variables to identify subsets of variables before executing anomaly detection algorithms locally for each of those subsets. Through multiple experimentations done on both synthetic and real datasets coming from the literature, we show the relevance of our approach with a significant improvement in anomaly detection performance.
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