  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.
