{"total":33,"rows":[{"id":"2410.12261","title":"CATCH: Channel-Aware multivariate Time Series Anomaly Detection via Frequency Patching","created":"2024-10-16","cited_by_count":94,"area":null},{"id":"2506.18046","title":"TAB: Unified Benchmarking of Time Series Anomaly Detection Methods","created":"2025-06-22","cited_by_count":60,"area":null},{"id":"2510.14510","title":"Enhancing Time Series Forecasting through Selective Representation Spaces: A Patch Perspective","created":"2025-10-16","cited_by_count":39,"area":null},{"id":"2410.19892","title":"Air Quality Prediction with Physics-Guided Dual Neural ODEs in Open Systems","created":"2024-10-25","cited_by_count":26,"area":null},{"id":"2503.13502","title":"Foundation Models for Spatio-Temporal Data Science: A Tutorial and Survey","created":"2025-03-12","cited_by_count":24,"area":null},{"id":"2510.12489","title":"CrossAD: Time Series Anomaly Detection with Cross-scale Associations and Cross-window Modeling","created":"2025-10-14","cited_by_count":12,"area":null},{"id":"2503.21200","title":"Learning Generalizable Skills from Offline Multi-Task Data for Multi-Agent Cooperation","created":"2025-03-27","cited_by_count":8,"area":null},{"id":"2510.16014","title":"STAR: Boosting Time Series Foundation Models for Anomaly Detection through State-aware Adapter","created":"2025-10-15","cited_by_count":7,"area":null},{"id":"2510.19710","title":"SEMPO: Lightweight Foundation Models for Time Series Forecasting","created":"2025-10-22","cited_by_count":7,"area":null},{"id":"2602.01359","title":"PaAno: Patch-Based Representation Learning for Time-Series Anomaly Detection","created":"2026-02-01","cited_by_count":7,"area":null},{"id":"2504.09993","title":"AimTS: Augmented Series and Image Contrastive Learning for Time Series Classification","created":"2025-04-14","cited_by_count":6,"area":null},{"id":"2603.14407","title":"Towards One-for-All Anomaly Detection for Tabular Data","created":"2026-03-15","cited_by_count":6,"area":null},{"id":"2509.18751","title":"Patch-based Memory Gate Model in Time Series Foundation Model","created":"2025-09-23","cited_by_count":4,"area":null},{"id":"2509.24378","title":"AXIS: Explainable Time Series Anomaly Detection with Large Language Models","created":"2025-09-29","cited_by_count":4,"area":null},{"id":"2602.05646","title":"Empowering Time Series Analysis with Large-Scale Multimodal Pretraining","created":"2026-02-05","cited_by_count":3,"area":null},{"id":"2603.21612","title":"Towards Multimodal Time Series Anomaly Detection with Semantic Alignment and Condensed Interaction","created":"2026-03-23","cited_by_count":3,"area":null},{"id":"2606.02670","title":"Anomalies in Multivariate Time Series Benchmarks Are Mostly Univariate","created":"2026-06-01","cited_by_count":3,"area":null},{"id":"2503.08473","title":"Data Driven Decision Making with Time Series and Spatio-temporal Data","created":"2025-03-11","cited_by_count":2,"area":null},{"id":"2511.20044","title":"RED-F: Reconstruction-Elimination based Dual-stream Contrastive Forecasting for Multivariate Time Series Anomaly Prediction","created":"2025-11-25","cited_by_count":2,"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":"2510.03911","title":"THEMIS: Unlocking Pretrained Knowledge with Foundation Model Embeddings for Anomaly Detection in Time Series","created":"2025-10-04","cited_by_count":0,"area":null},{"id":"2602.16681","title":"VETime: Vision Enhanced Zero-Shot Time Series Anomaly Detection","created":"2026-02-18","cited_by_count":0,"area":null},{"id":"2603.26842","title":"VAN-AD: Visual Masked Autoencoder with Normalizing Flow For Time Series Anomaly Detection","created":"2026-03-27","cited_by_count":0,"area":null},{"id":"2603.29183","title":"IMPACT: Influence Modeling for Open-Set Time Series Anomaly Detection","created":"2026-03-31","cited_by_count":0,"area":null},{"id":"2605.23504","title":"VACE: Learning Geometrically Structured Representations for Time Series Anomaly Detection","created":"2026-05-22","cited_by_count":0,"area":null},{"id":"2606.02138","title":"VLBM: Variational Latent Basis Modeling for OOD Robust Multivariate Time Series Forecasting","created":"2026-06-01","cited_by_count":0,"area":null},{"id":"2606.08935","title":"PAI: Preserving Amplitude Information in Representation-Based Time-Series Anomaly Detection","created":"2026-06-08","cited_by_count":0,"area":null},{"id":"2606.20055","title":"PaAno+: Multiscale Encoding and Cross-Variable Attention for Time Series Anomaly Detection","created":"2026-06-18","cited_by_count":0,"area":null},{"id":"2609.33610","title":"Supervision Recovery for Time Series Anomaly Detection via Context-Anchored Pairing","created":"2026-09-27","cited_by_count":0,"area":null},{"id":"2609.38004","title":"No Scale Left Behind: Multi-Scale Autoencoder with Bi-directional Attention for Time Series Anomaly Detection","created":"2026-09-29","cited_by_count":0,"area":null},{"id":"2609.38789","title":"LEARN-TS: LLM-Enhanced Alignment and Reconstruction with Normality Guidance for Multivariate Time-Series Anomaly Detection","created":"2026-09-30","cited_by_count":0,"area":null},{"id":"2610.00978","title":"Generalist Representation, Specialist Detection: TS-Router for Time-Series Anomaly Detection","created":"2026-10-01","cited_by_count":0,"area":null},{"id":"2610.06180","title":"Anlu: Enabling In-Context Time Series Anomaly Detection in Foundation Models via Counterfactual Supervision","created":"2026-10-05","cited_by_count":0,"area":null}],"next_cursor":null}