Anomaly Detection Using Multiscale Signatures

This paper analyzes multidimensional time series through the lens of their integrals of various moment orders, constituting their signatures, a novel tool for detecting anomalies in time series. The proposed anomaly detection (AD) method is compared using classical distance-based methods such as Local Outlier Factor (LOF) and One-Class Support Vector Machine (OCSVM). These methods are investigated using different similarity measures: distance on signature features, Euclidean distance and Dynamic Time Warping (DTW). The combination of signature features with a specific segmentation of time series leads to a multi-scale analysis tool that is competitive with respect to the state-of-the-art results, while maintaining low computational costs thanks to a property of the signature features.

Recommended citation: R. Mignot, V. Mangé, K. Usevich, M. Clausel, J.-Y. Tourneret, F. Vincent, "Anomaly Detection Using Multiscale Signatures", in Proc. EUSIPCO, Lyon, France, 2024
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