{"paper_id":"1898ebe1-6ba6-4a43-8729-7785f2e33aaa","body_text":"Unifying Global and Local Anomaly Detection for Time Series | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Unifying Global and Local Anomaly Detection for Time Series Tongkai Lu, Shuai Ma, Zhongxin Zhang, Lizhen Cui, Xuelian Lin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7450240/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Anomaly detection for time series data has been proven useful from both academia and industry, and can be categorized into global and local methodsbased on the range of data used to determine anomalies. To the best of our knowledge, there are no methods that unify the global and local methods together.In this study, we propose an approach that unifies the global and local anomaly detection to achieve the good performance. To make the unification possible, wediscretize time series data and partition the discrete data into global and local anomaly detection parts based on a concept of local factor. We design a rule-based global method using non-redundant association rules with tolerances to handle the difficulty of the latency phenomenon and the excessive number ofrules in time series data. We develop a Transformer based on local method with LSH (Locality Sensitive Hashing) Attention to fit better for discrete data. Wefinally conduct an extensive experimental study to verify the effectiveness and efficiency of our approach. Anomaly Detection Time Series Global Anomalies Local Anomalies Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 27 Feb, 2026 Reviews received at journal 17 Oct, 2025 Reviewers agreed at journal 29 Sep, 2025 Reviewers agreed at journal 05 Sep, 2025 Reviewers invited by journal 03 Sep, 2025 Editor assigned by journal 03 Sep, 2025 Submission checks completed at journal 25 Aug, 2025 First submitted to journal 25 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-7450240\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":510723169,\"identity\":\"c20df578-4fa1-4168-85b3-4afc6c238d99\",\"order_by\":0,\"name\":\"Tongkai Lu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Beihang University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Tongkai\",\"middleName\":\"\",\"lastName\":\"Lu\",\"suffix\":\"\"},{\"id\":510723170,\"identity\":\"37beb628-aff6-416c-a0e2-d740b1a608ce\",\"order_by\":1,\"name\":\"Shuai 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