{"total":27,"rows":[{"id":"2405.15370","title":"Large Language Models can Deliver Accurate and Interpretable Time Series Anomaly Detection","created":"2024-05-24","cited_by_count":29,"area":null},{"id":"2312.11988","title":"Xpert: Empowering Incident Management with Query Recommendations via Large Language Models","created":"2023-12-19","cited_by_count":22,"area":null},{"id":"2408.13082","title":"Multivariate Time-Series Anomaly Detection based on Enhancing Graph Attention Networks with Topological Analysis","created":"2024-08-23","cited_by_count":11,"area":null},{"id":"2504.14204","title":"DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection","created":"2025-04-19","cited_by_count":7,"area":null},{"id":"2505.20765","title":"Robust and Explainable Detector of Time Series Anomaly via Augmenting Multiclass Pseudo-Anomalies","created":"2025-05-27","cited_by_count":7,"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":"2402.01071","title":"Chameleon: Foundation Models for Fairness-aware Multi-modal Data Augmentation to Enhance Coverage of Minorities","created":"2024-02-02","cited_by_count":5,"area":null},{"id":"2507.13366","title":"Leveraging the Spatial Hierarchy: Coarse-to-fine Trajectory Generation via Cascaded Hybrid Diffusion","created":"2025-07-08","cited_by_count":4,"area":null},{"id":"2504.09504","title":"MADLLM: Multivariate Anomaly Detection via Pre-trained LLMs","created":"2025-04-13","cited_by_count":3,"area":null},{"id":"2510.18998","title":"An Encode-then-Decompose Approach to Unsupervised Time Series Anomaly Detection on Contaminated Training Data--Extended Version","created":"2025-10-21","cited_by_count":3,"area":null},{"id":"2406.19720","title":"CUPID: Improving Battle Fairness and Position Satisfaction in Online MOBA Games with a Re-matchmaking System","created":"2024-06-28","cited_by_count":2,"area":null},{"id":"2601.19255","title":"LLM-Assisted Logic Rule Learning: Scaling Human Expertise for Time Series Anomaly Detection","created":"2026-01-27","cited_by_count":2,"area":null},{"id":"2509.15153","title":"AnoF-Diff: One-Step Diffusion-Based Anomaly Detection for Forceful Tool Use","created":"2025-09-18","cited_by_count":1,"area":null},{"id":"2604.13924","title":"ASTER: Latent Pseudo-Anomaly Generation for Unsupervised Time-Series Anomaly Detection","created":"2026-04-15","cited_by_count":1,"area":null},{"id":"2404.12134","title":"Warped Time Series Anomaly Detection","created":"2024-04-18","cited_by_count":0,"area":null},{"id":"2506.20023","title":"DIM-SUM: Dynamic IMputation for Smart Utility Management","created":"2025-06-24","cited_by_count":0,"area":null},{"id":"2507.14224","title":"Diffusion-based translation between unpaired spontaneous premature neonatal EEG and fetal MEG","created":"2025-07-16","cited_by_count":0,"area":null},{"id":"2508.00881","title":"Hallucination Detection and Mitigation with Diffusion in Multi-Variate Time-Series Foundation Models","created":"2025-07-23","cited_by_count":0,"area":null},{"id":"2509.01098","title":"CCE: Confidence-Consistency Evaluation for Time Series Anomaly Detection","created":"2025-09-01","cited_by_count":0,"area":null},{"id":"2510.11084","title":"Causal Disentanglement Learning for Accurate Anomaly Detection in Multivariate Time Series","created":"2025-10-13","cited_by_count":0,"area":null},{"id":"2604.14726","title":"Catching Every Ripple: Enhanced Anomaly Awareness via Dynamic Concept Adaptation","created":"2026-04-16","cited_by_count":0,"area":null},{"id":"2605.09685","title":"Learning Unified Representations of Normalcy for Time Series Anomaly Detection","created":"2026-05-10","cited_by_count":0,"area":null},{"id":"2605.18128","title":"POST: Prior-Observation Adversarial Learning of Spatio-Temporal Associations for Multivariate Time Series Anomaly Detection","created":"2026-05-18","cited_by_count":0,"area":null},{"id":"2605.26193","title":"Bridging Classification and Reconstruction: Cooperative Time Series Anomaly Detection","created":"2026-05-25","cited_by_count":0,"area":null},{"id":"2606.04073","title":"TPA-AD: A Two-Stage Pseudo Anomaly-Guided Method for Bearing Time-Series Anomaly Detection","created":"2026-06-02","cited_by_count":0,"area":null},{"id":"2607.00720","title":"Detecting the Undetectable: Enhancing Unsupervised time series Anomaly Detection via Active Learning","created":"2026-07-01","cited_by_count":0,"area":null},{"id":"2610.12754","title":"NV-Tesseract-AD: Curriculum-Enhanced Conditional Diffusion for Multivariate Time-Series Anomaly Detection","created":"2026-10-08","cited_by_count":0,"area":null}],"next_cursor":null}