Time-Series Anomaly Detection

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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

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

  1. Quantifying and Prioritizing Stealthy Source-Manipulation Threats Against Multi-Source Anomaly Detectors

    Oct 27, 2026Indraveni Chebolu, Seema Panghal, Arnab Mallick +1Adversarial AttacksAnomaly Detection

    Information Security Group, Centre for Development of Advanced Computing, Hyderabad, Telangana, India

  2. Data Quality Audit and Bivariate Temperature-Humidity Analysis of a Long-Term LoRaWAN Network: Anomaly Detection, PCA, Clustering and Machine Learning

    Oct 23, 2026Mamadou SowTime-Series Anomaly DetectionAnomaly Detection

    LIPN-CNRS7030, Université Sorbonne Paris Nord, 99 avenue JB Clément, F-93430 Villetaneuse, France

  3. TENET: Time-point Encoding Network for Multivariate Time Series Anomaly Detection

    Oct 23, 2026Dongchan Cho, Juwon Hwang, Keumyeong Kang +2Unsupervised Anomaly DetectionTime-Series Anomaly Detection

    Industrial AI Lab, SimPlatform Co. Ltd. Affiliate Research Institute

  4. EVOLVE: Uncertainty-Aware Multivariate Time Series Anomaly Prediction with Adaptive Prototype Memories

    Oct 23, 2026Jianxin Jin, Kai Zhao, Shiyan Hu +8Time-Series Anomaly DetectionUnsupervised Anomaly Detection

    East China Normal University · Aalborg University · APPLab, Huawei +2

  5. Autoregressive Multimodal Anomaly Detection with Large Language Models for Microservice Systems

    Oct 22, 2026Yixiang Tang, Fagui Liu, Guoxiang Zhong +2Multimodal Anomaly DetectionAnomaly Detection

    School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China · Department of New Networks, Peng Cheng Laboratory, Shenzhen 518000, China

  6. Future of Time Series Anomaly Detection: From Historical Review to Unified Outlook

    Oct 22, 2026Zhijie Zhong, Zhiwen Yu, Pengyu Li +6Time-Series Anomaly DetectionAnomaly Detection

    School of Future Technology, South China University of Technology, Guangzhou, 510650, China · Pengcheng Laboratory, Shenzhen, 518066, China · School of Computer Science and Engineering, South China University of Technology, Guangzhou, Guangdong 510650, China +3

  7. Distilling Multi-Teacher Scoring Principles for Unsupervised Time Series Anomaly Detection

    Oct 21, 2026Dongchan Cho, Jiho Han, Juwon Hwang +3Unsupervised Anomaly DetectionTime-Series Anomaly Detection

    Industrial AI Lab, SimPlatform Co. Ltd. Affiliate Research Institute

  8. KPI-Aware Multimodal Anomaly Detection for 5G and Open RAN Telemetry

    Oct 18, 2026Chiman Salavati, Liang Wu, Kelly Wan +2Multimodal Anomaly DetectionTime-Series Anomaly Detection

    University of Connecticut, Storrs, Connecticut, USA · Nokia, Sunnyvale, California, USA

  9. HCFM: Helmholtz Component Flow Matching for Time Series Anomaly Detection

    Oct 18, 2026Shyam Sundar Saravanan, Tie Luo, Sanjay MadriaTime-Series Anomaly DetectionUnsupervised Anomaly Detection

    Department of Computer Science, Missouri University of Science and Technology, Rolla, MO, USA · Department of ECE, University of Kentucky, Lexington, KY, USA

  10. Retrieval Over Training: Similarity search-based Model Selection for Time Series Anomaly Detection

    Oct 14, 2026Christos Panourgias, Roberto Stanzione, Adrien Petralia +2Model SelectionTime-Series Anomaly Detection

    Université Paris Cité, LIPADE · École Normale Supérieure, Inria

  11. OneShot: Label-Efficient Detector Weighting for Time Series Anomaly Detection

    Oct 12, 2026Seunghan Lee, Jun Seo, Jaehoon Lee +9Time-Series Anomaly DetectionAnomaly Detection

    LG AI Research

  12. Benchmarking Deep Learning Models for Anomaly Detection in Real-World Cloud Telemetry

    Oct 11, 2026Sumit Dhyani, Sougata MukherjeaTime-Series Anomaly DetectionAnomaly Detection

    Electrical Engineering Department, Indian Institute of Technology, Delhi, India

  13. Correlation-Aware Reconstruction for Multivariate Time Series Anomaly Detection

    Oct 11, 2026Md. Johir Raihan, Zahra Zamanzadeh Darban, Yueyang Liu +3Time-Series Anomaly DetectionAnomaly Detection

    Monash University, Melbourne, Victoria, Australia · University of New South Wales, Sydney, New South Wales, Australia

  14. CESNET-TimeSeries26: A Multi-Year Network Traffic Time Series Dataset for Annual Seasonality, Concept Drift, and Long-Term Anomaly Detection

    Oct 10, 2026Josef Koumar, Karel Hynek, Kamil Jerabek +1Forecasting BenchmarksLong-Term Time Series Forecasting

    Czech Technical University in Prague, Thákurova 9, 160 00 Prague 6, Czech Republic · CESNET a.l.e., Generála Píky 430/26, 160 00 Prague 6, Czech Republic

  15. NV-Tesseract-AD: Curriculum-Enhanced Conditional Diffusion for Multivariate Time-Series Anomaly Detection

    Oct 8, 2026Aditi Gautam, Manikandan Ravikiran, Saira Qureshi +4Time-Series Anomaly DetectionReconstruction-Based Anomaly Detection

    NVIDIA

  16. Quantum anomaly detection in real scarce data

    Oct 7, 2026Emanuele Casciaro, Fabio Mascherpa, Alfonso Amendola +1Anomaly DetectionTime-Series Anomaly Detection

    Department of Physics and Astrophysics, University of Florence, Via Sansone, 1, Sesto Fiorentino, 50019, Italy · DICOX/C High Performance Computing Center of Excellence, DIT Digital & Information Technology, Eni S.p.A., Via Emilia 1, San Donato Milanese, 20097, Italy

  17. Temporal transformer CAN encoder with federated lightweight heads for anomaly detection

    Oct 7, 2026Konstantinos Gyftodimos, Kyriakos Chiotis, Elena Politi +2Time-Series Anomaly DetectionCybersecurity

    ANADELTA P.C., Athens, Greece · Department of Informatics and Telematics, Harokopio University of Athens, Greece

  18. MORA: Modeling Observed Changes for Drift-Robust Time-Series Anomaly Detection

    Oct 7, 2026Xudong Mou, Tiejun Wang, Rui Wang +5Time-Series Anomaly DetectionNon-Stationary Time Series

    Beihang University, Beijing, China · Beihang Hangzhou Innovation Institute, Hangzhou, China · China University of Geosciences (Beijing), Beijing, China

  19. Normality Constraint Learning: Adapting Foundation Models for Time Series Anomaly Detection

    Oct 5, 2026Xiaohui Zhou, Yijie Wang, Hongzuo Xu +2Reconstruction-Based Anomaly DetectionTime-Series Anomaly Detection

    National Key Laboratory of Parallel and Distributed Computing · College of Computer Science and Technology, National University of Defense Technology · Intelligent Game and Decision Lab (IGDL) +1

  20. Anlu: Enabling In-Context Time Series Anomaly Detection in Foundation Models via Counterfactual Supervision

    Oct 5, 2026Tian Lan, Yifei Gao, Yimeng Lu +5LLM-Based Time Series Anomaly DetectionTime-Series Anomaly Detection

    Department of Industrial Engineering, Tsinghua University · Huawei

  21. A Data-Driven Framework for Unsupervised Monitoring of Transmission Systems Using End-of-Line Testing Data: A Case Study at Ford Motor Company

    Oct 4, 2026Mohammad N. Bisheh, Mehrdad Moradi, Parinaz Farajiparvar +6Industrial Anomaly DetectionTime-Series Anomaly Detection

    Georgia Tech, Atlanta, Georgia, USA · Ford Motor Company, Dearborn, Michigan, USA

  22. Smart Sensing for Safer Bridges: From Sensor Signals to AI-Driven Anomaly Detection

    Oct 2, 2026Rahul Jaiswal, Joakim Hellum, Halvor HeibergOnline Anomaly DetectionTime-Series Anomaly Detection

    Smart Sensor Systems AS Oslo, Norway

  23. A Two-Stage Cascade for Near-Real-Time Forest Anomaly Detection from Sentinel-1 SAR Time Series

    Oct 2, 2026Pann Thinzar Seint, Subas Chhatkuli, Bryan AtwoodRemote Sensing Change DetectionTime-Series Anomaly Detection

    DAI Labs, K.K.

  24. Validated Data Onboarding for AI Demand Forecasting on U.S. Building Meter Data: Design, Controlled Evaluation, and a Corrected Negative Result

    Oct 1, 2026Yixuan LiangElectricity Load ForecastingTime Series Imputation

    Independent researcher

  25. Have an LLM Write Your Anomaly Detector: Autonomous Discovery of Compact, Interpretable Detectors for Time Series

    Oct 1, 2026David BerghausInterpretable Anomaly DetectionLLM-Based Program Synthesis

    Lamarr Institute Germany · Fraunhofer IAIS Germany

  26. Detect, Explain, Interpret: An End-to-End Benchmark for Time Series Anomaly Detection, Explainability and Interpretability

    Oct 1, 2026Roberto Stanzione, Jules Barbe, Magali Parrino +2Interpretable Anomaly DetectionAnomaly Localization

    Inria, ENS, CNRS, PSL Paris, France · Scality Paris, France · Inria, ENS, CNRS, PSL, EDF Paris, France

  27. Generalist Representation, Specialist Detection: TS-Router for Time-Series Anomaly Detection

    Oct 1, 2026Tian Lan, Yifei Gao, Yimeng Lu +6Unsupervised Anomaly DetectionTime-Series Anomaly Detection

    Department of Industrial Engineering Tsinghua University · Huawei · Datadog AI Research Paris, France

  28. From Benchmarks to Production: Transferring Time Series Anomaly Detection Methods for Electricity Production Monitoring

    Sep 30, 2026Nicolas Vautier, Paul Caron, Nardi Xhepi +4Interpretable Anomaly DetectionIndustrial Anomaly Detection

    EDF Lab Paris Saclay · EDF, DOAAT · Inria, Ecole Normale Sup´erieure (PSL), CNRS

  29. What Streaming Anomaly Detection Finds (and Misses) in Industrial Time Series

    Sep 30, 2026Magali Parrino, Antoine Ajenjo, Emmanuel Remy +2Online Anomaly DetectionIndustrial Anomaly Detection

    EDF R&D, France · Inria, ENS, CNRS, PSL, France

  30. In a Streaming World, Should You Stand Still? A Comprehensive Benchmark of Anomaly Detection in Streams

    Sep 30, 2026Magali Parrino, Antoine Ajenjo, Emmanuel Remy +3Online Anomaly DetectionConcept Drift

    EDF R&D Chatou, France · ENS, PSL University, CNRS, Inria Paris, France · DI ENS, ENS, PSL University, CNRS, Inria Paris, France