[2603.29183] IMPACT: Influence Modeling for Open-Set Time Series Anomaly Detection

Computer Science > Machine Learning arXiv:2603.29183 (cs) [Submitted on 31 Mar 2026] Title:IMPACT: Influence Modeling for Open-Set Time Series Anomaly Detection Authors:Xiaohui Zhou, Yijie Wang, Hongzuo Xu, Weixuan Liang, Xiaoli Li, Guansong Pang View a PDF of the paper titled IMPACT: Influence Modeling for Open-Set Time Series Anomaly Detection, by Xiaohui Zhou and 5 other authors View PDF HTML (experimental) Abstract:Open-set anomaly detection (OSAD) is an emerging paradigm designed to utilize limited labeled data from anomaly classes seen in training to identify both seen and unseen anomalies during testing. Current approaches rely on simple augmentation methods to generate pseudo anomalies that replicate unseen anomalies. Despite being promising in image data, these methods are found to be ineffective in time series data due to the failure to preserve its sequential nature, resulting in trivial or unrealistic anomaly patterns. They are further plagued when the training data is contaminated with unlabeled anomalies. This work introduces $\textbf{IMPACT}$, a novel framework that leverages $\underline{\textbf{i}}$nfluence $\underline{\textbf{m}}$odeling for o$\underline{\textbf{p}}$en-set time series $\underline{\textbf{a}}$nomaly dete$\underline{\textbf{ct}}$ion, to tackle these challenges. The key insight is to $\textbf{i)}$ learn an influence function that can accurately estimate the impact of individual training samples on the modeling, and then $\textbf{ii)}$ leverag...