Signature-Kernel One-Class Learning with Manifold Regularisation for Multivariate Time Series Anomaly Detection

One-class learning is an effective methodology for time series anomaly detection, yet kernel one-class approaches often compare sequences in a pointwise fashion and ignore the geometry of normal data. In this work, we present a manifold-regularised, large-margin $\ell_p$-support vector data descript