{"total":16,"rows":[{"id":"2410.05440","title":"Can LLMs Understand Time Series Anomalies?","created":"2024-10-07","cited_by_count":41,"area":null},{"id":"2401.09793","title":"PatchAD: A Lightweight Patch-based MLP-Mixer for Time Series Anomaly Detection","created":"2024-01-18","cited_by_count":12,"area":null},{"id":"2404.18948","title":"Sub-Adjacent Transformer: Improving Time Series Anomaly Detection with Reconstruction Error from Sub-Adjacent Neighborhoods","created":"2024-04-27","cited_by_count":10,"area":null},{"id":"2405.11238","title":"SimAD: A Simple Dissimilarity-based Approach for Time Series Anomaly Detection","created":"2024-05-18","cited_by_count":6,"area":null},{"id":"2509.02341","title":"RDIT: Residual-based Diffusion Implicit Models for Probabilistic Time Series Forecasting","created":"2025-09-02","cited_by_count":4,"area":null},{"id":"2501.11959","title":"Noise-Resilient Point-wise Anomaly Detection in Time Series Using Weak Segment Labels","created":"2025-01-21","cited_by_count":3,"area":null},{"id":"2401.03341","title":"Weakly Augmented Variational Autoencoder in Time Series Anomaly Detection","created":"2024-01-07","cited_by_count":2,"area":null},{"id":"2410.24178","title":"AR-Pro: Counterfactual Explanations for Anomaly Repair with Formal Properties","created":"2024-10-31","cited_by_count":1,"area":null},{"id":"2501.05675","title":"Synergizing Large Language Models and Task-specific Models for Time Series Anomaly Detection","created":"2025-01-10","cited_by_count":1,"area":null},{"id":"2506.00188","title":"Cluster-Aware Causal Mixer for Online Anomaly Detection in Multivariate Time Series","created":"2025-05-30","cited_by_count":1,"area":null},{"id":"2509.12650","title":"Leveraging Intermediate Representations of Time Series Foundation Models for Anomaly Detection","created":"2025-09-16","cited_by_count":1,"area":null},{"id":"2509.20184","title":"An Improved Time Series Anomaly Detection by Applying Structural Similarity","created":"2025-09-24","cited_by_count":0,"area":null},{"id":"2510.22619","title":"CLEANet: Robust and Efficient Anomaly Detection in Contaminated Multivariate Time Series","created":"2025-10-26","cited_by_count":0,"area":null},{"id":"2602.01635","title":"COMET: Codebook-based Online-adaptive Multi-scale Embedding for Time-series Anomaly Detection","created":"2026-02-02","cited_by_count":0,"area":null},{"id":"2602.08467","title":"Low Rank Transformer for Multivariate Time Series Anomaly Detection and Localization","created":"2026-02-09","cited_by_count":0,"area":null},{"id":"2609.34161","title":"GT-PSSM: Unified Probabilistic Framework for Stochastic Dynamics Modeling and Dependency Learning in Multivariate Time Series Anomaly Detection","created":"2026-09-28","cited_by_count":0,"area":null}],"next_cursor":null}