Time-Series Anomaly Detection

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12 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

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Causally-Constrained Probabilistic Forecasting for Time-Series Anomaly Detection
Apr 20, 2026Pooyan Khosravinia, João Gama, Bruno VelosoTime-Series Anomaly Detection Causal Graph
Conditional Attribution for Root Cause Analysis in Time-Series Anomaly Detection
Apr 19, 2026Shashank Mishra, Karan Patil, Cedric Schockaert +2Time-Series Anomaly Detection Interpretable Anomaly Detection
German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany · Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau, Germany · Paul Wurth S.A, Luxembourg
Back to Repair: A Minimal Denoising Network for Time Series Anomaly Detection
Apr 19, 2026Kadir-Kaan Özer, René Ebeling, Markus EnzweilerTime-Series Anomaly Detection Time Series
Mercedes-Benz AG, Germany · Institute for Intelligent Systems, Esslingen University of Applied Sciences, Germany
Fun-TSG: A Function-Driven Multivariate Time Series Generator with Variable-Level Anomaly Labeling
Apr 14, 2026Pierre Lotte, André Péninou, Olivier TesteTime-Series Anomaly Detection Time-Series Generation
EPE UT, IRIT · Université de Toulouse, IRIT, CNRS · UT2J, IRIT-SIG, IRIT +2
VAN-AD: Visual Masked Autoencoder with Normalizing Flow For Time Series Anomaly Detection
Mar 27, 2026PengYu Chen, Shang Wan, Xiaohou Shi +3Time-Series Anomaly Detection Unsupervised Detection
School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China · China Telecom Research Institute Beijing, China · Department of Computer Science, Missouri University of Science and Technology, Rolla, MO 65409 USA
Hyperspectral Trajectory Image for Multi-Month Trajectory Anomaly Detection
Mar 26, 2026Md Awsafur Rahman, Chandrakanth Gudavalli, Hardik Prajapati +1Time-Series Anomaly Detection Unsupervised Detection
UC Santa Barbara, USA
Anomaly detection in time-series via inductive biases in the latent space of conditional normalizing flows
Mar 12, 2026David Baumgartner, Eliezer de Souza da Silva, Iñigo UrteagaTime-Series Anomaly Detection Multivariate Time-Series
Department of Computer Science, Norwegian University of Science and Technology, Norway · BCAM — Basque Center for Applied Mathematics, Spain · Department of Informatics Engineering, University of Coimbra, Portugal +2
LEFT: Learnable Fusion of Tri-view Tokens for Unsupervised Time Series Anomaly Detection
Feb 9, 2026Dezheng Wang, Tong Chen, Guansong Pang +3Time-Series Anomaly Detection Cross-View Fusion
School of Automation, Southeast University Nanjing, China · School of Electrical Engineering and Computer Science, The University of Queensland Brisbane, Australia · School of Computing and Information Systems, Singapore Management University Singapore
PaAno: Patch-Based Representation Learning for Time-Series Anomaly Detection
Feb 1, 2026Jinju Park, Seokho KangTime-Series Anomaly Detection Time Series
Department of Industrial Engineering, Sungkyunkwan University
Weighted Contrastive Learning for Anomaly-Aware Time-Series Forecasting
Dec 8, 2025Joel Ekstrand, Tor Mattsson, Zahra Taghiyarrenani +3Time-Series Anomaly Detection Contrastive Learning
Center for Applied Intelligent Systems Research, Halmstad University, Halmstad, Sweden · Mikael Linden Consulting AB, Stockholm, Sweden
COGNOS: Universal Enhancement for Time Series Anomaly Detection via Constrained Gaussian-Noise Optimization and Smoothing
Nov 10, 2025Wenlong Shang, Shihao Tian, Xutong Wan +1Time-Series Anomaly Detection Anomaly Score
School of Information Science and Technology, Beijing University of Technology, China · Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, China

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Causally-Constrained Probabilistic Forecasting for Time-Series Anomaly Detection

Pooyan Khosravinia, João Gama, Bruno Veloso

Time-Series Anomaly Detection Causal Graph

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Anomaly detection in multivariate time series is a central challenge in industrial monitoring, as failures frequently arise from complex temporal dynamics and cross-sensor interactions. While recent deep learning models, including graph neural networks and Transformers, have demonstrated strong empirical performance, most approaches remain primarily correlational and offer limited support for causal interpretation and root-cause localization. This study introduces a causally-constrained probabilistic forecasting framework which is a Causally Guided Transformer (CGT) model for multivariate time-series anomaly detection, integrating an explicit time-lagged causal graph prior with deep sequence modeling. For each target variable, a dedicated forecasting block employs a hard parent mask derived from causal discovery to restrict the main prediction pathway to graph-supported causes, while a latent Gaussian head captures predictive uncertainty. To leverage residual correlational information without compromising the causal representation, a shadow auxiliary path with stop-gradient isolation and a safety-gated blending mechanism is incorporated to suppress non-causal contributions when reliability is low. Anomalies are identified using negative log-likelihood scores with adaptive streaming thresholding, and root-cause variables are determined through per-dimension probabilistic attribution and counterfactual clamping. Experiments on the ASD and SMD benchmarks indicate that the proposed method achieves state-of-the-art detection performance, with F1-scores of 96.19% on ASD and 95.32% on SMD, and enhances variable-level attribution quality. These findings suggest that causal structural priors can improve both robustness and interpretability in detecting deep anomalies in multivariate sensor systems.

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