Warped Time Series Anomaly Detection
Charlotte Lacoquelle, Xavier Pucel, Louise Travé-Massuyès, Axel Reymonet, Benoît Enaux · IEEE Transactions on Signal Processing · 2026
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. Given the targeted industrial context, there is a strong requirement for frugality both in terms of input data and method parametrization. Consequently, the proposed approach remains lightweight, robust, and largely self-sufficient to ensure practical deployment and ease of maintenance. 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 thatWETSANDscales to large signals, computes human-friendly prototypes, works with very little data, and outperforms some general purpose anomaly detection approaches such as autoencoders.