{"total":76,"rows":[{"id":"2510.23672","title":"DBLoss: Decomposition-based Loss Function for Time Series Forecasting","created":"2025-10-27","cited_by_count":45,"area":null},{"id":"2507.05631","title":"OFFSET: Segmentation-based Focus Shift Revision for Composed Image Retrieval","created":"2025-07-08","cited_by_count":40,"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":"2512.02792","title":"HUD: Hierarchical Uncertainty-Aware Disambiguation Network for Composed Video Retrieval","created":"2025-12-02","cited_by_count":32,"area":null},{"id":"2412.17603","title":"EasyTime: Time Series Forecasting Made Easy","created":"2024-12-23","cited_by_count":30,"area":null},{"id":"2506.06005","title":"LightGTS: A Lightweight General Time Series Forecasting Model","created":"2025-06-06","cited_by_count":29,"area":null},{"id":"2509.22295","title":"Aurora: Towards Universal Generative Multimodal Time Series Forecasting","created":"2025-09-26","cited_by_count":29,"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":"2509.14181","title":"Bridging Past and Future: Distribution-Aware Alignment for Time Series Forecasting","created":"2025-09-17","cited_by_count":20,"area":null},{"id":"2603.08032","title":"GCGNet: Graph-Consistent Generative Network for Time Series Forecasting with Exogenous Variables","created":"2026-03-09","cited_by_count":13,"area":null},{"id":"2506.23121","title":"CRISP-SAM2: SAM2 with Cross-Modal Interaction and Semantic Prompting for Multi-Organ Segmentation","created":"2025-06-29","cited_by_count":12,"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":"2602.00582","title":"Bridging Time and Frequency: A Joint Modeling Framework for Irregular Multivariate Time Series Forecasting","created":"2026-01-31","cited_by_count":9,"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":"2505.23017","title":"$K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting","created":"2025-05-29","cited_by_count":8,"area":null},{"id":"2509.23313","title":"ASTGI: Adaptive Spatio-Temporal Graph Interactions for Irregular Multivariate Time Series Forecasting","created":"2025-09-27","cited_by_count":8,"area":null},{"id":"2510.25207","title":"Selective Learning for Deep Time Series Forecasting","created":"2025-10-29","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":"2602.01359","title":"PaAno: Patch-Based Representation Learning for Time-Series Anomaly Detection","created":"2026-02-01","cited_by_count":7,"area":null},{"id":"2501.15385","title":"DDUNet: Dual Dynamic U-Net for Highly-Efficient Cloud Segmentation","created":"2025-01-26","cited_by_count":6,"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":"2501.01342","title":"DeepFilter: A Transformer-style Framework for Accurate and Efficient Process Monitoring","created":"2025-01-02","cited_by_count":5,"area":null},{"id":"2510.16511","title":"Structured Temporal Causality for Interpretable Multivariate Time Series Anomaly Detection","created":"2025-10-18","cited_by_count":4,"area":null},{"id":"2501.15389","title":"CP2M: Clustered-Patch-Mixed Mosaic Augmentation for Aerial Image Segmentation","created":"2025-01-26","cited_by_count":3,"area":null},{"id":"2512.14253","title":"FLAME: Flow Enhanced Legendre Memory Models for General Time Series Forecasting","created":"2025-12-16","cited_by_count":3,"area":null},{"id":"2602.00654","title":"PHAT: Modeling Period Heterogeneity for Multivariate Time Series Forecasting","created":"2026-01-31","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":"2604.01845","title":"CANDI: Curated Test-Time Adaptation for Multivariate Time-Series Anomaly Detection Under Distribution Shift","created":"2026-04-02","cited_by_count":2,"area":null},{"id":"2609.24444","title":"WPBench: A Comprehensive Benchmark for Wind Power Forecasting","created":"2026-09-21","cited_by_count":2,"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":"2506.02576","title":"ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting","created":"2025-06-03","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":"2510.01970","title":"Moon: A Modality Conversion-based Efficient Multivariate Time Series Anomaly Detection","created":"2025-10-02","cited_by_count":1,"area":null},{"id":"2511.06894","title":"COGNOS: Universal Enhancement for Time Series Anomaly Detection via Constrained Gaussian-Noise Optimization and Smoothing","created":"2025-11-10","cited_by_count":1,"area":null},{"id":"2512.14718","title":"SEED: Spectral Entropy-Guided Evaluation of SpatialTemporal Dependencies for Multivariate Time Series Forecasting","created":"2025-12-09","cited_by_count":1,"area":null},{"id":"2602.19068","title":"TimeRadar: A Domain-Rotatable Foundation Model for Time Series Anomaly Detection","created":"2026-02-22","cited_by_count":1,"area":null},{"id":"2605.17730","title":"L-Drive: Beyond a Single Mapping-Latent Context Drives Time Series Forecasting","created":"2026-05-18","cited_by_count":1,"area":null},{"id":"2607.15799","title":"Knowledge-Assisted Multi-Graph Dependency Learning for Multivariate Time Series Anomaly Detection in Multi-Stage Industrial Processes","created":"2026-07-17","cited_by_count":1,"area":null},{"id":"2609.15483","title":"GSLAD: Prototype-Regularized Graph Structure Learning for Multivariate Time Series Anomaly Detection","created":"2026-09-14","cited_by_count":1,"area":null},{"id":"2609.34842","title":"QiYao-M: Multimodal Time Series Foundation Model with Role-Aware Modeling of Endogenous and Exogenous Modalities","created":"2026-09-28","cited_by_count":1,"area":null},{"id":"2504.14178","title":"Segregation and Context Aggregation Network for Real-time Cloud Segmentation","created":"2025-04-19","cited_by_count":0,"area":null},{"id":"2509.06419","title":"CAPMix: Robust KPI Anomaly Detection for AIOps in Noisy and Dynamic Environments","created":"2025-09-08","cited_by_count":0,"area":null},{"id":"2509.19985","title":"Pi-transformer: A prior-informed dual-attention model for multivariate time-series anomaly detection","created":"2025-09-24","cited_by_count":0,"area":null},{"id":"2511.18846","title":"WaveTuner: Comprehensive Wavelet Subband Tuning for Time Series Forecasting","created":"2025-11-24","cited_by_count":0,"area":null},{"id":"2601.12288","title":"TimeGMM: Single-Pass Probabilistic Forecasting via Adaptive Gaussian Mixture Models with Reversible Normalization","created":"2026-01-18","cited_by_count":0,"area":null},{"id":"2601.15731","title":"FAIR-ESI: Feature Adaptive Importance Refinement for Electrophysiological Source Imaging","created":"2026-01-22","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":"2602.08638","title":"LEFT: Learnable Fusion of Tri-view Tokens for Unsupervised Time Series Anomaly Detection","created":"2026-02-09","cited_by_count":0,"area":null},{"id":"2603.06131","title":"DQE: A Semantic-Aware Evaluation Metric for Time Series Anomaly Detection","created":"2026-03-06","cited_by_count":0,"area":null},{"id":"2603.24254","title":"Beyond Static Uncertainty: Modeling Temporal Uncertainty Dynamics for Probabilistic Time Series Forecasting","created":"2026-03-25","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":"2604.08582","title":"Multivariate Time Series Anomaly Detection via Dual-Branch Reconstruction and Autoregressive Flow-based Residual Density Estimation","created":"2026-03-29","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":"2605.23744","title":"Contrast to Detect: Dynamic Graph Contrastive Regularization for Unsupervised Anomaly Detection in Multivariate Time Series","created":"2026-05-22","cited_by_count":0,"area":null},{"id":"2605.31259","title":"Lightweight CNN-Based Anomaly Detection for High Voltage Converter Modulators in the Spallation Neutron Source","created":"2026-05-29","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":"2606.18729","title":"TimeLAVA: Learning-Agnostic Valuation for Time Series Data","created":"2026-06-17","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":"2607.06094","title":"Modeling Normal Is All You Need: Joint Latent Clustering for Anomaly Detection in Multimodal Cyber-Physical Systems","created":"2026-07-07","cited_by_count":0,"area":null},{"id":"2607.23197","title":"Domain-Prior-Regularized Graph Modeling for Anomaly Detection in Cyber-Physical Systems","created":"2026-07-25","cited_by_count":0,"area":null},{"id":"2608.01885","title":"CARE: A Cascaded Framework for Efficient and Reliable Time Series Anomaly Detection","created":"2026-08-03","cited_by_count":0,"area":null},{"id":"2608.19966","title":"Rethinking Patch Based Multivariate Time Series Forecasting with Semantic Structured Partitioning","created":"2026-08-20","cited_by_count":0,"area":null},{"id":"2609.24441","title":"MUSE: Dependency-Aware Adaptation of a Frozen Vision Backbone for Multivariate Time Series Forecasting","created":"2026-09-21","cited_by_count":0,"area":null},{"id":"2609.31038","title":"Aurora-X: Built for Extreme Time Series Forecasting","created":"2026-09-25","cited_by_count":0,"area":null},{"id":"2609.31157","title":"Bayesian Tensor Autoencoder with Physics-informed Predictive Prior for Multi-dimensional Time Series Anomaly Detection","created":"2026-09-25","cited_by_count":0,"area":null},{"id":"2609.31470","title":"Beyond Empirical Support: Structured Outlier Generation via Sinkhorn Optimal Transport","created":"2026-09-25","cited_by_count":0,"area":null},{"id":"2609.34939","title":"XMatch: Enhancing Covariate-Aware Time Series Forecasting through Tree-Structured Exogenous Matching","created":"2026-09-28","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":"2610.01223","title":"Have an LLM Write Your Anomaly Detector: Autonomous Discovery of Compact, Interpretable Detectors for Time Series","created":"2026-10-01","cited_by_count":0,"area":null},{"id":"2610.02661","title":"AIGS: Adaptive Incremental Gating System for Online Representation Learning in Non-Stationary Data Streams","created":"2026-10-02","cited_by_count":0,"area":null},{"id":"2610.06936","title":"QiYao-I: A Manifold Based Foundation Model for Irregular Multivariate Time Series Forecasting","created":"2026-10-03","cited_by_count":0,"area":null},{"id":"2610.15250","title":"Correlation-Aware Reconstruction for Multivariate Time Series Anomaly Detection","created":"2026-10-11","cited_by_count":0,"area":null}],"next_cursor":null}