Time-Series Anomaly Detection

Ask about this topic

Overview

Time-series anomaly detection seeks unusual points, intervals, or changes in relationships among signals, often when labeled failures are scarce and early warnings matter. Common approaches forecast or reconstruct normal behavior and flag large residuals; others use density models, learned representations, graphs, causal structure, or ensembles to capture dependencies and changing operating regimes. Research spans univariate and multivariate benchmarks as well as industrial, medical, network, and edge-device settings, with growing attention to localization, explanations, and compute limits. A central tension is that results depend heavily on evaluation metrics, thresholds, windowing, and anomaly types, while strong benchmark scores may not carry over to distribution shifts or real-time deployment.

Latest papers

CardsList

208 papers35 papers in the last four weeks, up 169% on the four weeks before. 0.2% of all new papers.

  1. Modeling Normal Is All You Need: Joint Latent Clustering for Anomaly Detection in Multimodal Cyber-Physical Systems

    Jul 7, 2026Alexander Apartsin, Yehudit ApersteinMultimodal Anomaly DetectionIndustrial Anomaly Detection

    Holon Institute of Technology (HIT), Holon, Israel · Afeka Academic College of Engineering, Tel Aviv, Israel

  2. Predictive Conformal Slip Monitoring: An Empirical Evaluation of Rolling Split Conformal Prediction for Pre-Incident Traction Loss Detection

    Jul 2, 2026Varshith Roy KotlaOnline Anomaly DetectionConformal Prediction

    Department of CSE, Faculty of Science and Technology, The ICFAI Foundation for Higher Education, Hyderabad, India

  3. Fast and Accurate Anomaly Detection in Time Series

    Jul 2, 2026Emanuele Mele, Massimo Cafaro, Angelo Coluccia +1Unsupervised Anomaly DetectionTime-Series Anomaly Detection

    University of Salento, Via per Monteroni, Lecce, 73100, , Italy

  4. Detecting the Undetectable: Enhancing Unsupervised time series Anomaly Detection via Active Learning

    Jul 1, 2026Seung Hun Han, Hyeongwon Kang, Jinwoo Park +1Industrial Anomaly DetectionActive Learning

    LG CNS, 71 Magokjungang 8-ro, Gangseo-gu, Seoul, Republic of Korea · Department of Industrial & Management Engineering, Korea University, 126-16 Anam-dong 5-ga, Seongbuk-gu, Seoul, Republic of Korea · Department of Industrial Engineering, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul, Republic of Korea

  5. Distributed Hierarchical Temporal Memory with Shared Associative Memory for Cross-Entity Preemptive Warning

    Jun 30, 2026Pavia Bera, Jennifer Adorno, Sanjukta BhanjaOnline Anomaly DetectionCross-Domain Transfer Learning

    University of South Florida, Tampa, FL, USA

  6. Redefining Maritime Anomaly Detection via Equation-Grounded Synthetic Anomalies

    Jun 29, 2026Youngseok Hwang, Sungho Bae, Dohun Lee +4Synthetic Anomaly GenerationTime-Series Anomaly Detection

    Seoul National University Seoul, Republic of Korea · KRISO Daejeon, Republic of Korea

  7. An Agentic AI Pipeline for Appliance-Level Energy Anomaly Detection and LLM-Driven Recommendations

    Jun 26, 2026Dihia Falouz, Aida Douaibia, Amine Bechar +3Retrieval-Augmented GenerationLLM Agent Evaluation

    Laboratoire LITAN École supérieure en Sciences et Technologies de l’Informatique et du Numérique RN 75, Amizour 06300, Bejaia, Algérie · College of Computing and Informatics University of Sharjah Sharjah, UAE · College of Engineering and Information Technology (CEIT) University of Dubai Dubai, UAE

  8. Weighted Score-Oriented Losses for Temporally Localized Event Prediction

    Jun 22, 2026Edoardo Legnaro, Sabrina Guastavino, Francesco MarchettiChange-Point DetectionTime-Series Anomaly Detection

    Department of Mathematics, University of Genoa Via Dodecaneso 35, 16146 Genoa, Italy · Department of Mathematics “Tullio Levi-Civita”, University of Padua Via Trieste 63, 35121 Padua, Italy

  9. Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection

    Jun 20, 2026Weizhi Nie, Weichao Liu, Weijie Wang +1Few-Shot Anomaly DetectionWorld Model Learning

    Tianjin University

  10. Distinguishing indistinguishable attractors: Unsupervised anomaly detection with reservoir computers

    Jun 19, 2026Davide Prosperino, Haochun Ma, Christoph RäthOnline Anomaly DetectionReservoir Computing

    1Ludwig-Maximilians-Universität München, Faculty of Physics, Munich, 80539, Germany · 2Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institute for Frontier Materials on Earth and in Space, Cologne, 51170, Germany

  11. PaAno+: Multiscale Encoding and Cross-Variable Attention for Time Series Anomaly Detection

    Jun 18, 2026Youji Zhu, Hongbing Wang, Wenchao Liu +2Self-Supervised Time Series Representation LearningTime-Series Anomaly Detection

    School of Mathematical Sciences, Guizhou Normal University, Guiyang 550025, China. · School of Big Data and Computer Science, Guizhou Normal University, Guiyang 550025, China.

  12. SCAN: Enhance Time Series Anomaly Detection via Multi-Scale Neighborhood-Centered Clustering

    Jun 17, 2026Xingze Zheng, Hanyin Cheng, Siyuan Wang +4Multi-View ClusteringReconstruction-Based Anomaly Detection

    East China Normal University · APPLab, Huawei · Huawei

  13. Anomaly Detection for Sparse and Irregular Multivariate Time Series with Latent SDEs

    Jun 17, 2026Martin Uray, Dominik Geng, Florian Graf +2Irregular Time-Series ModelingLatent Dynamics Modeling

    Josef Ressel Centre for Intelligent and Secure Industrial Automation, University of Applied Sciences, Salzburg, Austria · University of Salzburg, Austria

  14. CRAFTIIF: Cross-Resolution Analytic Four-Type Interpretable Isolation Forest for Multivariate Time Series Anomaly Detection

    Jun 11, 2026William SmitsIsolation ForestInterpretable Anomaly Detection

    Avathon, Austin, TX, USA

  15. Rarity-Gated Context Conditioning for Offline Imitation Learning-Based Maritime Anomaly Detection

    Jun 11, 2026Yongmin Kim, ByeongHoon Jeon, Sungil KimTime-Series Anomaly DetectionAnomaly Detection

    Department of Industrial Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea.

  16. ProtoX-AD: Self-Explainable Time Series Anomaly Detection and Characterization

    Jun 11, 2026Aitor Sánchez-Ferrera, Elisabeth Wetzer, Kristoffer Wickstrøm +2Prototype-Based LearningLLM-Based Time Series Anomaly Detection

    aDepartment of Computer Science and Artificial Intelligence, University of the Basque Country UPV/EHU, Donostia, Spain · bDepartment of Physics and Technology, UiT The Arctic University of Norway, Tromsø, Norway

  17. APEX: A Network-Native Time-Series Foundation Model for Forecasting and Anomaly Detection for Wireless Edge Operations

    Jun 10, 2026Swadhin Pradhan, Niloo Bahadori, Peiman AminiWireless CommunicationsWireless Foundation Models

    Cisco Systems, USA

  18. The Distributed Detectability Band Against Marginal-Preserving Attacks

    Jun 9, 2026Zhang Qinqin, Gao YuzeAdversarial AttacksAI Agent Security

  19. PAI: Preserving Amplitude Information in Representation-Based Time-Series Anomaly Detection

    Jun 8, 2026Kang Zhang, Wei Jian Lau, Shoushou Ren +3Time Series Representation LearningTime-Series Anomaly Detection

    1HUAWEI · 2KAIST

  20. Anomaly Detection and Root Cause Analysis for Microservice Systems

    Jun 8, 2026Luan PhamSoftware EngineeringMicroservice Architecture

    Viet Nam National University Ho Chi Minh City - University of Technology (HCMUT) · School of Computing Technologies, College of Science, Technology, Engineering and Maths, Royal Melbourne Institute of Technology (RMIT University), Melbourne, Victoria, Australia

  21. CF-JEPA: Mask-free forward prediction with asymmetric encoder utilization for time-series representation learning

    Jun 5, 2026Jaehoon Lee, Sunghyun SimMultivariate Time Series ForecastingTime Series Classification

    aGraduate School of Artificial Intelligence Convergence Engineering, Changwon National University, Changwon, South Korea · bDepartment of Artificial Intelligence Engineering, Changwon National University, Changwon, South Korea

  22. Attack Detection using Time Series Foundation Models

    Jun 4, 2026Sribalaji C. Anand, Anh Tung Nguyen, George J. PappasCyber-Physical SystemsCybersecurity

    University of Pennsylvania, United States · Uppsala University, Sweden

  23. DAST: A VLM-LLM Framework for Cross-Interface Anomaly Detection in O-RAN

    Jun 4, 2026Francesco Spinelli, Esteban Municio, Pau Baguer +2Wireless CommunicationsOpen Radio Access Network

    i2CAT Foundation · NEC Laboratories Europe · ICREA

  24. GenAutoML: An Agentic Framework for Dynamic Architecture Generation and Optimization in Time-Series Analysis

    Jun 4, 2026Oleeviya Babu Poikarayil, Cédric Schockaert, Abdulrahman Nahhas +3Efficient Neural Network InferenceLarge Language Model-Guided NAS

    Paul Wurth S.A., Luxembourg · Otto-von-Guericke University, Magdeburg, Germany · Technical University of Munich, Germany

  25. T-SAR-JEPA: Self-Supervised Temporal Anomaly Detection in SAR Amplitude Stacks via Latent Prediction

    Jun 4, 2026Kerod Woldesenbet, Abem WoldesenbetSelf-Supervised Time Series Representation LearningRemote Sensing

    Independent Researcher · Dakota State University

  26. Anomaly Detection for Electro-Hydrostatic Actuators using LSTM Autoencoder

    Jun 3, 2026Nehal Afifi, Abdelmonem Elhendawi, Felix Leitenberger +2Industrial Anomaly DetectionReconstruction-Based Anomaly Detection

    IPEK - Institute of Product Engineering, Karlsruhe Institute of Technology (KIT), Germany. · SUPMICROTECH-ENSMM, France.

  27. Disjoint or Overlapping? Inference Windowing for Reconstruction-Based Time Series Anomaly Detection

    Jun 2, 2026Guillaume Coulaud, Reza Akbarinia, Florent MassegliaReconstruction-Based Anomaly DetectionTime-Series Anomaly Detection

    UM, IROKO · University of Montpellier, Inria, CNRS, LIRMM, Montpellier, France · IROKO +1

  28. TPA-AD: A Two-Stage Pseudo Anomaly-Guided Method for Bearing Time-Series Anomaly Detection

    Jun 2, 2026Xiancheng Wang, Zhibo Zhang, Ran Li +4Fault DetectionUnsupervised Anomaly Detection

    School of Ocean Engineering, Harbin Institute of Technology, West Wenhua Road, Weihai, 264209, Shandong, China · Technical Center, Bogie Development Department, CRRC Qingdao Sifang Locomotive and Rolling Stock Co., Ltd, Jinhong East Road, Qingdao, 266111, Shandong, China · Qingdao University, No. 308 Ningxia Road, Qingdao, 266071, Shandong, China

  29. Trans GAN-WT: A Feature Extraction and Interactive Learning-Based Anomaly Detection Model for Wind Turbine Time Series Data

    Jun 2, 2026Jingzhe KangReconstruction-Based Anomaly DetectionTime-Series Anomaly Detection

    School of Mathematics, Shandong University, Jinan 250100, China

  30. Anomalies in Multivariate Time Series Benchmarks Are Mostly Univariate

    Jun 1, 2026Marc Pinet, Julien Cumin, Samuel Berlemont +1Time-Series Anomaly Detection

    Orange Research, Meylan, France · Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG, 38000 Grenoble, France