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In this paper, we begin by introducing a unifying framework for benchmarking unsupervised anomaly detection (AD) methods, and highlight the problem of shifts in normal behaviors that can occur in practical AIOps scenarios. To tackle anomaly detection under domain shift, we then cast the problem in the framework of domain generalization and propose a novel approach, Domain-Invariant VAE for Anomaly Detection (DIVAD), to learn domain-invariant representations for unsupervised anomaly detection. Our evaluation results using the Exathlon benchmark show that the two main DIVAD variants significantly outperform the best unsupervised AD method in maximum performance, with 20% and 15% improvements in maximum peak F1-scores, respectively. Evaluation using the Application Server Dataset further demonstrates the broader applicability of our domain generalization methods.<\/jats:p>","DOI":"10.14778\/3725688.3725699","type":"journal-article","created":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T14:19:21Z","timestamp":1756477161000},"page":"1691-1704","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Unsupervised Anomaly Detection in Multivariate Time Series across Heterogeneous Domains"],"prefix":"10.14778","volume":"18","author":[{"given":"Vincent","family":"Jacob","sequence":"first","affiliation":[{"name":"Ecole Polytechnique, Palaiseau, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanlei","family":"Diao","sequence":"additional","affiliation":[{"name":"Ecole Polytechnique, Palaiseau, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,8,29]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-47578-3_1"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-47578-3_3"},{"key":"e_1_2_1_3_1","volume-title":"Machine Learning and Knowledge Discovery in Databases, Ulf Brefeld, Elisa Fromont, Andreas Hotho, Arno Knobbe, Marloes Maathuis, and C\u00e9line Robardet (Eds.)","author":"Akuzawa Kei","unstructured":"Kei Akuzawa, Yusuke Iwasawa, and Yutaka Matsuo. 2020. 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