This paper addresses the problem of detecting time series outliers, focusing on systems with repetitive behavior, such as industrial robots operating on production lines.Notable challenges arise from the fact that a task performed multiple times may exhibit different duration in each repetition and that the time series reported by the sensors are irregularly sampled because of data gaps. The anomaly detection approach presented in this paper consists of three stages.The first stage identifies the repetitive cycles in the lengthy time series and segments them into individual time series corresponding to one task cycle, while accounting for possible temporal distortions.The second stage computes a prototype for the cycles using a GPU-based barycenter algorithm, specifically tailored for very large time series.The third stage uses the prototype to detect abnormal cycles by computing an anomaly score for each cycle.The overall approach, named WarpEd Time Series ANomaly Detection (WETSAND), makes use of the Dynamic Time Warping algorithm and its variants because they are suited to the distorted nature of the time series.The experiments show that \wetsand scales to large signals, computes human-friendly prototypes, works with very little data, and outperforms some general purpose anomaly detection approaches such as autoencoders.

翻译：本文研究了时间序列异常值检测问题，重点关注具有重复性行为的系统，例如在生产线中运行的工业机器人。显著的挑战在于：同一任务多次执行时每次持续时长可能不同，且传感器报告的时间序列因数据缺失而呈现不规则采样。本文提出的异常检测方法包含三个阶段。第一阶段识别长时间序列中的重复周期，并将其分割为对应单个任务周期的独立时间序列，同时考虑可能的时间形变。第二阶段利用基于GPU的重心算法（专门针对超大规模时间序列设计）计算周期的原型模板。第三阶段通过计算每个周期的异常分数，借助原型模板检测异常周期。该整体方法命名为“扭曲时间序列异常检测”（WETSAND），采用动态时间规整算法及其变体，因其适用于时间序列的扭曲特性。实验表明，WETSAND可扩展至大规模信号、生成人类友好的原型模板、在极少量数据下有效工作，且性能优于自编码器等通用异常检测方法。