{"total":95,"rows":[{"id":"2208.09399","title":"Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models","created":"2022-08-19","cited_by_count":198,"area":null},{"id":"2307.03759","title":"A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection","created":"2023-07-07","cited_by_count":145,"area":null},{"id":"2302.09746","title":"PriSTI: A Conditional Diffusion Framework for Spatiotemporal Imputation","created":"2023-02-20","cited_by_count":66,"area":"diffusion"},{"id":"2205.13479","title":"Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations","created":"2022-05-26","cited_by_count":64,"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":"2209.06520","title":"Scalable Spatiotemporal Graph Neural Networks","created":"2022-09-14","cited_by_count":52,"area":null},{"id":"2312.01728","title":"ImputeFormer: Low Rankness-Induced Transformers for Generalizable Spatiotemporal Imputation","created":"2023-12-04","cited_by_count":50,"area":null},{"id":"2305.05890","title":"CUTS+: High-dimensional Causal Discovery from Irregular Time-series","created":"2023-05-10","cited_by_count":41,"area":null},{"id":"2302.04071","title":"Taming Local Effects in Graph-based Spatiotemporal Forecasting","created":"2023-02-08","cited_by_count":37,"area":null},{"id":"2310.01753","title":"CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discovery","created":"2023-10-03","cited_by_count":36,"area":null},{"id":"2302.07458","title":"CUTS: Neural Causal Discovery from Irregular Time-Series Data","created":"2023-02-15","cited_by_count":33,"area":null},{"id":"2406.12747","title":"TSI-Bench: Benchmarking Time Series Imputation","created":"2024-06-18","cited_by_count":33,"area":null},{"id":"2305.18612","title":"Networked Time Series Imputation via Position-aware Graph Enhanced Variational Autoencoders","created":"2023-05-29","cited_by_count":32,"area":null},{"id":"2310.15978","title":"Graph Deep Learning for Time Series Forecasting","created":"2023-10-24","cited_by_count":26,"area":null},{"id":"2205.13492","title":"Sparse Graph Learning from Spatiotemporal Time Series","created":"2022-05-26","cited_by_count":22,"area":null},{"id":"2311.02565","title":"KITS: Inductive Spatio-Temporal Kriging with Increment Training Strategy","created":"2023-11-05","cited_by_count":19,"area":null},{"id":"2305.19183","title":"Graph-based Time Series Clustering for End-to-End Hierarchical Forecasting","created":"2023-05-30","cited_by_count":17,"area":null},{"id":"2402.10634","title":"Graph-based Forecasting with Missing Data through Spatiotemporal Downsampling","created":"2024-02-16","cited_by_count":17,"area":null},{"id":"2201.00818","title":"Graph Neural Networks for Multivariate Time Series Regression with Application to Seismic Data","created":"2022-01-03","cited_by_count":15,"area":null},{"id":"2505.17431","title":"HyperIMTS: Hypergraph Neural Network for Irregular Multivariate Time Series Forecasting","created":"2025-05-23","cited_by_count":15,"area":null},{"id":"2311.03309","title":"Neural Structure Learning with Stochastic Differential Equations","created":"2023-11-06","cited_by_count":14,"area":null},{"id":"2306.00488","title":"Reconstructing Graph Diffusion History from a Single Snapshot","created":"2023-06-01","cited_by_count":13,"area":null},{"id":"2401.02143","title":"Graph Neural Networks for Tabular Data Learning: A Survey with Taxonomy and Directions","created":"2024-01-04","cited_by_count":13,"area":null},{"id":"2403.19800","title":"Gegenbauer Graph Neural Networks for Time-varying Signal Reconstruction","created":"2024-03-28","cited_by_count":12,"area":null},{"id":"2403.11960","title":"Causality-Aware Spatiotemporal Graph Neural Networks for Spatiotemporal Time Series Imputation","created":"2024-03-18","cited_by_count":11,"area":null},{"id":"2502.09443","title":"Relational Conformal Prediction for Correlated Time Series","created":"2025-02-13","cited_by_count":11,"area":null},{"id":"2411.12164","title":"Diffusion Transformers as Open-World Spatiotemporal Foundation Models","created":"2024-11-19","cited_by_count":10,"area":"diffusion"},{"id":"2211.11596","title":"Forecasting Unobserved Node States with spatio-temporal Graph Neural Networks","created":"2022-11-21","cited_by_count":9,"area":null},{"id":"2510.04910","title":"Glocal Information Bottleneck for Time Series Imputation","created":"2025-10-06","cited_by_count":9,"area":null},{"id":"2310.09620","title":"Machine Learning for Urban Air Quality Analytics: A Survey","created":"2023-10-14","cited_by_count":8,"area":null},{"id":"2402.12598","title":"Graph-based Virtual Sensing from Sparse and Partial Multivariate Observations","created":"2024-02-19","cited_by_count":8,"area":null},{"id":"2405.10995","title":"Higher-order Spatio-temporal Physics-incorporated Graph Neural Network for Multivariate Time Series Imputation","created":"2024-05-16","cited_by_count":8,"area":null},{"id":"2406.12282","title":"SAGDFN: A Scalable Adaptive Graph Diffusion Forecasting Network for Multivariate Time Series Forecasting","created":"2024-06-18","cited_by_count":8,"area":null},{"id":"2302.08415","title":"Temporal Graph Neural Networks for Irregular Data","created":"2023-02-16","cited_by_count":7,"area":null},{"id":"2309.01988","title":"sasdim: self-adaptive noise scaling diffusion model for spatial time series imputation","created":"2023-09-05","cited_by_count":7,"area":"diffusion"},{"id":"2509.23494","title":"Revisiting Multivariate Time Series Forecasting with Missing Values","created":"2025-09-27","cited_by_count":7,"area":null},{"id":"2404.08061","title":"Physics-Enhanced Graph Neural Networks For Soft Sensing in Industrial Internet of Things","created":"2024-04-11","cited_by_count":6,"area":null},{"id":"2405.13075","title":"Score-CDM: Score-Weighted Convolutional Diffusion Model for Multivariate Time Series Imputation","created":"2024-05-21","cited_by_count":6,"area":"diffusion"},{"id":"2408.17258","title":"Joint Estimation and Prediction of City-wide Delivery Demand: A Large Language Model Empowered Graph-based Learning Approach","created":"2024-08-30","cited_by_count":6,"area":null},{"id":"2503.00900","title":"S4M: S4 for multivariate time series forecasting with Missing values","created":"2025-03-02","cited_by_count":6,"area":null},{"id":"2204.11135","title":"AZ-whiteness test: a test for uncorrelated noise on spatio-temporal graphs","created":"2022-04-23","cited_by_count":5,"area":null},{"id":"2207.03932","title":"Memory-free Online Change-point Detection: A Novel Neural Network Approach","created":"2022-07-08","cited_by_count":5,"area":null},{"id":"2208.06573","title":"GEDI: A Graph-based End-to-end Data Imputation Framework","created":"2022-08-13","cited_by_count":5,"area":null},{"id":"2405.07233","title":"OXYGENERATOR: Reconstructing Global Ocean Deoxygenation Over a Century with Deep Learning","created":"2024-05-12","cited_by_count":5,"area":null},{"id":"2409.09930","title":"Mining of Switching Sparse Networks for Missing Value Imputation in Multivariate Time Series","created":"2024-09-16","cited_by_count":5,"area":null},{"id":"2301.01741","title":"Graph State-Space Models and Latent Relational Inference","created":"2023-01-04","cited_by_count":4,"area":null},{"id":"2402.11558","title":"A Temporally Disentangled Contrastive Diffusion Model for Spatiotemporal Imputation","created":"2024-02-18","cited_by_count":4,"area":"diffusion"},{"id":"2410.15248","title":"FastSTI: A Fast Conditional Pseudo Numerical Diffusion Model for Spatio-temporal Traffic Data Imputation","created":"2024-10-20","cited_by_count":4,"area":"diffusion"},{"id":"2506.12412","title":"Cross-Domain Conditional Diffusion Models for Time Series Imputation","created":"2025-06-14","cited_by_count":4,"area":"diffusion"},{"id":"2508.16161","title":"STA-GANN: A Valid and Generalizable Spatio-Temporal Kriging Approach","created":"2025-08-22","cited_by_count":4,"area":null},{"id":"2408.10878","title":"Trajectory Imputation in Multi-Agent Sports with Derivative-Accumulating Self-Ensemble","created":"2024-08-20","cited_by_count":3,"area":null},{"id":"2507.20362","title":"MH-GIN: Multi-scale Heterogeneous Graph-based Imputation Network for AIS Data (Extended Version)","created":"2025-07-27","cited_by_count":3,"area":null},{"id":"2606.06682","title":"Spatiotemporal Imputation with Graph-Informed Flow Matching","created":"2026-06-04","cited_by_count":3,"area":"diffusion"},{"id":"2407.05671","title":"MSTF: Multiscale Transformer for Incomplete Trajectory Prediction","created":"2024-07-08","cited_by_count":2,"area":null},{"id":"2409.08917","title":"Latent Space Score-based Diffusion Model for Probabilistic Multivariate Time Series Imputation","created":"2024-09-13","cited_by_count":2,"area":"diffusion"},{"id":"2501.12281","title":"MoGERNN: An Inductive Traffic Predictor for Unobserved Locations","created":"2025-01-21","cited_by_count":2,"area":null},{"id":"2506.08054","title":"STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation","created":"2025-06-09","cited_by_count":2,"area":"moe"},{"id":"2512.15116","title":"FADTI: Fourier and Attention Driven Diffusion for Multivariate Time Series Imputation","created":"2025-12-17","cited_by_count":2,"area":null},{"id":"2601.04855","title":"Rethinking GNNs and Missing Features: Challenges, Evaluation and a Robust Solution","created":"2026-01-08","cited_by_count":2,"area":null},{"id":"2602.21043","title":"T1: One-to-One Channel-Head Binding for Multivariate Time-Series Imputation","created":"2026-02-24","cited_by_count":2,"area":null},{"id":"2602.22532","title":"Coarse-to-Fine Learning of Dynamic Causal Structures","created":"2026-02-26","cited_by_count":2,"area":null},{"id":"2209.07798","title":"DBT-DMAE: An Effective Multivariate Time Series Pre-Train Model under Missing Data","created":"2022-09-16","cited_by_count":1,"area":null},{"id":"2305.12932","title":"Forecasting Irregularly Sampled Time Series using Graphs","created":"2023-05-22","cited_by_count":1,"area":null},{"id":"2410.05916","title":"TIMBA: Time series Imputation with Bi-directional Mamba Blocks and Diffusion models","created":"2024-10-08","cited_by_count":1,"area":"diffusion"},{"id":"2410.14630","title":"On the Regularization of Learnable Embeddings for Time Series Forecasting","created":"2024-10-18","cited_by_count":1,"area":null},{"id":"2505.07180","title":"Causal View of Time Series Imputation: Some Identification Results on Missing Mechanism","created":"2025-05-12","cited_by_count":1,"area":null},{"id":"2506.07099","title":"Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion","created":"2025-06-08","cited_by_count":1,"area":"diffusion"},{"id":"2602.20677","title":"UrbanFM: Scaling Urban Spatio-Temporal Foundation Models","created":"2026-02-24","cited_by_count":1,"area":null},{"id":"2605.02278","title":"HELIX: Hybrid Encoding with Learnable Identity and Cross-dimensional Synthesis for Time Series Imputation","created":"2026-05-04","cited_by_count":1,"area":null},{"id":"2605.16099","title":"Federated Imputation under Heterogeneous Feature Spaces","created":"2026-05-15","cited_by_count":1,"area":null},{"id":"2605.17316","title":"Learning Higher-Order Interactions from Incomplete Spatiotemporal Data with Mixtures of Hypergraph Laplacians","created":"2026-05-17","cited_by_count":1,"area":null},{"id":"2309.17335","title":"Asynchronous Graph Generator","created":"2023-09-29","cited_by_count":0,"area":null},{"id":"2312.00963","title":"Spatiotemporal Transformer for Imputing Sparse Data: A Deep Learning Approach","created":"2023-12-01","cited_by_count":0,"area":null},{"id":"2408.02689","title":"Spatio-Temporal Partial Sensing Forecast for Long-term Traffic","created":"2024-08-02","cited_by_count":0,"area":null},{"id":"2410.03954","title":"SDA-GRIN for Adaptive Spatial-Temporal Multivariate Time Series Imputation","created":"2024-10-04","cited_by_count":0,"area":null},{"id":"2410.21006","title":"A Review of Graph-Powered Data Quality Applications for IoT Monitoring Sensor Networks","created":"2024-10-28","cited_by_count":0,"area":null},{"id":"2412.00165","title":"Modelling Networked Dynamical System by Temporal Graph Neural ODE with Irregularly Partial Observed Time-series Data","created":"2024-11-29","cited_by_count":0,"area":null},{"id":"2412.04733","title":"A Survey and Benchmarking of Spatial-Temporal Traffic Data Imputation Models","created":"2024-12-06","cited_by_count":0,"area":null},{"id":"2503.09646","title":"Inductive Spatio-Temporal Kriging with Physics-Guided Increment Training Strategy for Air Quality Inference","created":"2025-03-12","cited_by_count":0,"area":null},{"id":"2505.19193","title":"SuperMAN: Interpretable and Expressive Networks over Temporally Sparse Heterogeneous Data","created":"2025-05-25","cited_by_count":0,"area":null},{"id":"2508.01240","title":"RelMap: Reliable Spatiotemporal Sensor Data Visualization via Imputative Spatial Interpolation","created":"2025-08-02","cited_by_count":0,"area":null},{"id":"2510.02216","title":"Diffusion Transformers for Imputation: Statistical Efficiency and Uncertainty Quantification","created":"2025-10-02","cited_by_count":0,"area":"diffusion"},{"id":"2510.24027","title":"Spatio-temporal Multivariate Time Series Forecast with Chosen Variables","created":"2025-10-28","cited_by_count":0,"area":null},{"id":"2510.24217","title":"Closing Gaps: An Imputation Analysis of ICU Vital Signs","created":"2025-10-28","cited_by_count":0,"area":null},{"id":"2602.09824","title":"PlugSI: Plug-and-Play Test-Time Graph Adaptation for Spatial Interpolation","created":"2026-02-10","cited_by_count":0,"area":null},{"id":"2603.05301","title":"Uniform Inductive Spatio-Temporal Kriging","created":"2026-03-05","cited_by_count":0,"area":null},{"id":"2603.29837","title":"DiSGMM: A Method for Time-varying Microscopic Weight Completion on Road Networks","created":"2026-03-31","cited_by_count":0,"area":null},{"id":"2604.15833","title":"Modern Structure-Aware Simplicial Spatiotemporal Neural Network","created":"2026-04-17","cited_by_count":0,"area":null},{"id":"2605.07577","title":"Bilevel Graph Structure Learning, Revisited: Inner-Channel Origins of the Reported Gain","created":"2026-05-08","cited_by_count":0,"area":null},{"id":"2605.19172","title":"Bridge: Retrieval-Augmented Spatiotemporal Modeling for Urban Delivery Demand","created":"2026-05-18","cited_by_count":0,"area":null},{"id":"2607.05613","title":"SafeImpute: Reliable Clinical Data Imputation via Conformal Selection","created":"2026-07-06","cited_by_count":0,"area":null},{"id":"2608.02366","title":"GLAIM: Learning Global and Local Adaptive Inter-Variable Dependency for Multivariate Time Series Imputation","created":"2026-08-03","cited_by_count":0,"area":null},{"id":"2609.11648","title":"RDDMPI: Residual Denoising Diffusion Model for Probabilistic Multivariate Time Series Imputation","created":"2026-09-10","cited_by_count":0,"area":"diffusion"},{"id":"2609.33089","title":"Geometry-Aware Operator Families for Structured Representation Learning","created":"2026-09-27","cited_by_count":0,"area":null},{"id":"2610.03147","title":"TSGuard: A Real-Time Framework for Detecting and Imputing Missing Data in Streaming Time Series","created":"2026-10-02","cited_by_count":0,"area":null}],"next_cursor":null}