针对网络时序数据中的异常行为，提出可解释的检测框架。
Robust Group Anomaly Detection for Quasi-Periodic Network Time Series
- 将时序数据映射为高斯混合模型，捕捉周期性模式差异
- 在多个公开数据集上显著优于现有方法
- 支持人类专家理解异常判断依据，适合工业监控场景
大量真实世界的多变量时序数据来自嵌入软件、电子元件和传感器的物理设备网络。这些设备生成的准周期信号通常具有相似的重复与周期性特征，但存在周期长度变化及因同步误差导致的时间长度不一。面对海量此类准周期时序数据，能否构建机器学习模型识别出与多数观测行为异常的序列？同时，模型能否帮助人类专家理解判断逻辑？本文提出序列到高斯混合模型（seq2GMM）框架，旨在从网络时序数据库中发现异常且值得关注的序列。进一步设计基于代理的优化算法，高效训练seq2GMM模型。该框架在多个公开基准数据集上表现出色，显著优于当前最优异常检测技术。我们还对所提训练算法的收敛性进行理论分析，并通过数值实验验证理论结论。
原文摘要 · Abstract (English)
Many real-world multivariate time series are collected from a network of physical objects embedded with software, electronics, and sensors. The quasi-periodic signals generated by these objects often follow a similar repetitive and periodic pattern, but have variations in the period, and come in different lengths caused by timing (synchronization) errors. Given a multitude of such quasi-periodic time series, can we build machine learning models to identify those time series that behave differently from the majority of the observations? In addition, can the models help human experts to understand how the decision was made? We propose a sequence to Gaussian Mixture Model (seq2GMM) framework. The overarching goal of this framework is to identify unusual and interesting time series within a network time series database. We further develop a surrogate-based optimization algorithm that can efficiently train the seq2GMM model. Seq2GMM exhibits strong empirical performance on a plurality of public benchmark datasets, outperforming state-of-the-art anomaly detection techniques by a significant margin. We also theoretically analyze the convergence property of the proposed training algorithm and provide numerical results to substantiate our theoretical claims.
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