{"total":25,"rows":[{"id":"2406.08627","title":"Time-MMD: Multi-Domain Multimodal Dataset for Time Series Analysis","created":"2024-06-12","cited_by_count":74,"area":null},{"id":"2402.04059","title":"Deep Learning for Multivariate Time Series Imputation: A Survey","created":"2024-02-06","cited_by_count":57,"area":null},{"id":"2310.07446","title":"ProbTS: Benchmarking Point and Distributional Forecasting across Diverse Prediction Horizons","created":"2023-10-11","cited_by_count":36,"area":null},{"id":"2401.03717","title":"Universal Time-Series Representation Learning: A Survey","created":"2024-01-08","cited_by_count":30,"area":null},{"id":"2310.01327","title":"TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series","created":"2023-10-02","cited_by_count":26,"area":null},{"id":"2310.15290","title":"Reliable Generation of Privacy-preserving Synthetic Electronic Health Record Time Series via Diffusion Models","created":"2023-10-23","cited_by_count":24,"area":null},{"id":"2311.01139","title":"Add and Thin: Diffusion for Temporal Point Processes","created":"2023-11-02","cited_by_count":23,"area":null},{"id":"2306.05880","title":"Time Series Continuous Modeling for Imputation and Forecasting with Implicit Neural Representations","created":"2023-06-09","cited_by_count":17,"area":null},{"id":"2509.19975","title":"From Samples to Scenarios: A New Paradigm for Probabilistic Forecasting","created":"2025-09-24","cited_by_count":10,"area":null},{"id":"2506.17039","title":"LSCD: Lomb-Scargle Conditioned Diffusion for Time series Imputation","created":"2025-06-20","cited_by_count":8,"area":null},{"id":"2410.12652","title":"Constrained Posterior Sampling: Time Series Generation with Hard Constraints","created":"2024-10-16","cited_by_count":7,"area":null},{"id":"2502.19049","title":"In-Context Learning of Stochastic Differential Equations with Foundation Inference Models","created":"2025-02-26","cited_by_count":7,"area":null},{"id":"2502.06645","title":"Koopman-Equivariant Gaussian Processes","created":"2025-02-10","cited_by_count":6,"area":null},{"id":"2407.08442","title":"How Deep is your Guess? A Fresh Perspective on Deep Learning for Medical Time-Series Imputation","created":"2024-07-11","cited_by_count":5,"area":null},{"id":"2410.07550","title":"Conditional Lagrangian Wasserstein Flow for Time Series Imputation","created":"2024-10-10","cited_by_count":4,"area":null},{"id":"2505.22573","title":"FNOPE: Simulation-based inference on function spaces with Fourier Neural Operators","created":"2025-05-28","cited_by_count":4,"area":null},{"id":"2403.14133","title":"3D Object Detection from Point Cloud via Voting Step Diffusion","created":"2024-03-21","cited_by_count":3,"area":null},{"id":"2501.17770","title":"Generative Unordered Flow for Set-Structured Data Generation","created":"2025-01-29","cited_by_count":2,"area":null},{"id":"2505.23215","title":"A Jump-Diffusion Framework for Irregular Time Series Generation","created":"2025-05-29","cited_by_count":2,"area":null},{"id":"2311.05417","title":"Predicting the Position Uncertainty at the Time of Closest Approach with Diffusion Models","created":"2023-11-09","cited_by_count":1,"area":null},{"id":"2507.13207","title":"MoTM: Towards a Foundation Model for Time Series Imputation based on Continuous Modeling","created":"2025-07-17","cited_by_count":1,"area":null},{"id":"2505.05381","title":"Towards High Resolution Probabilistic Coastal Inundation Forecasting from Sparse Observations","created":"2025-05-08","cited_by_count":0,"area":null},{"id":"2601.22848","title":"Unconditional flow-based time series generation with equivariance-regularised latent spaces","created":"2026-01-30","cited_by_count":0,"area":null},{"id":"2604.17670","title":"Prior-Fitted Functional Flow: In-Context Generative Models for Pharmacokinetics","created":"2026-04-19","cited_by_count":0,"area":null},{"id":"2605.28507","title":"Universal Time Series Generation with Neural Controlled Differential Equations","created":"2026-05-27","cited_by_count":0,"area":null}],"next_cursor":null}