RoLA：一种面向多变量时间序列的实时在线轻量级异常检测系统
RoLA: A Real-Time Online Lightweight Anomaly Detection System for Multivariate Time Series
AI总结：
本文提出RoLA系统，通过分治策略、并行处理和多数表决规则，利用多个轻量级检测器实时在线检测多变量时间序列异常，并在公开数据集上验证了其检测精度与轻量级性能。
摘要：
多变量时间序列是指在一段时间内同时从某个设备或系统采集的两个或更多变量的观测值。为了确保系统正常运行和良好的服务质量，实时监控多变量时间序列并检测异常的需求日益增长。同时，人们也非常希望拥有一种轻量级异常检测系统，该系统能够考虑不同变量之间的相关性、适应多变量时间序列模式的变化、提供即时响应，并基于无监督学习和在线模型训练为检测结果提供支持性信息。在过去十年中，已经提出了许多多变量时间序列异常检测方法。然而，它们无法同时提供上述所有特性。在本文中，我们提出了RoLA，一种基于分治策略、并行处理和多数表决规则的面向多变量时间序列的实时在线轻量级异常检测系统。RoLA采用多个轻量级异常检测器并行监控多变量时间序列，动态地实时确定变量之间的相关性，然后基于多数表决规则联合实时检测异常。为了展示RoLA的性能，我们基于One Ocean Expedition的FerryBox提供的公开数据集进行了实验。结果表明，RoLA提供了令人满意的检测精度和轻量级性能。
英文摘要：
A multivariate time series refers to observations of two or more variables taken from a device or a system simultaneously over time. There is an increasing need to monitor multivariate time series and detect anomalies in real time to ensure proper system operation and good service quality. It is also highly desirable to have a lightweight anomaly detection system that considers correlations between different variables, adapts to changes in the pattern of the multivariate time series, offers immediate responses, and provides supportive information regarding detection results based on unsupervised learning and online model training. In the past decade, many multivariate time series anomaly detection approaches have been introduced. However, they are unable to offer all the above-mentioned features. In this paper, we propose RoLA, a real-time online lightweight anomaly detection system for multivariate time series based on a divide-and-conquer strategy, parallel processing, and the majority rule. RoLA employs multiple lightweight anomaly detectors to monitor multivariate time series in parallel, determine the correlations between variables dynamically on the fly, and then jointly detect anomalies based on the majority rule in real time. To demonstrate the performance of RoLA, we conducted an experiment based on a public dataset provided by the FerryBox of the One Ocean Expedition. The results show that RoLA provides satisfactory detection accuracy and lightweight performance.
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。
