{"total":503,"rows":[{"id":"2110.13985","title":"Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers","created":"2021-10-26","cited_by_count":608,"area":null},{"id":"2101.10318","title":"Multi-Time Attention Networks for Irregularly Sampled Time Series","created":"2021-01-25","cited_by_count":238,"area":null},{"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":"2407.13278","title":"Deep Time Series Models: A Comprehensive Survey and Benchmark","created":"2024-07-18","cited_by_count":197,"area":null},{"id":"2202.08516","title":"SAITS: Self-Attention-based Imputation for Time Series","created":"2022-02-17","cited_by_count":169,"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":"2209.12951","title":"Liquid Structural State-Space Models","created":"2022-09-26","cited_by_count":136,"area":null},{"id":"2102.02611","title":"CKConv: Continuous Kernel Convolution For Sequential Data","created":"2021-02-04","cited_by_count":120,"area":null},{"id":"2112.06409","title":"Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective","created":"2021-12-13","cited_by_count":116,"area":null},{"id":"2110.05357","title":"Graph-Guided Network for Irregularly Sampled Multivariate Time Series","created":"2021-10-11","cited_by_count":111,"area":null},{"id":"2111.11344","title":"Modeling Irregular Time Series with Continuous Recurrent Units","created":"2021-11-22","cited_by_count":103,"area":null},{"id":"2110.13040","title":"Neural Flows: Efficient Alternative to Neural ODEs","created":"2021-10-25","cited_by_count":101,"area":null},{"id":"2107.14293","title":"Self-Supervised Transformer for Sparse and Irregularly Sampled Multivariate Clinical Time-Series","created":"2021-07-29","cited_by_count":96,"area":null},{"id":"2108.00298","title":"Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks","created":"2021-07-31","cited_by_count":95,"area":null},{"id":"2303.12799","title":"Time Series as Images: Vision Transformer for Irregularly Sampled Time Series","created":"2023-03-01","cited_by_count":79,"area":null},{"id":"2301.03150","title":"MOTOR: A Time-To-Event Foundation Model For Structured Medical Records","created":"2023-01-09","cited_by_count":78,"area":null},{"id":"2111.08585","title":"CEHR-BERT: Incorporating temporal information from structured EHR data to improve prediction tasks","created":"2021-11-10","cited_by_count":76,"area":null},{"id":"2108.13461","title":"Time Series Prediction using Deep Learning Methods in Healthcare","created":"2021-08-30","cited_by_count":74,"area":null},{"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":"2106.13898","title":"Closed-form Continuous-time Neural Models","created":"2021-06-25","cited_by_count":65,"area":null},{"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":"2402.10635","title":"ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling","created":"2024-02-16","cited_by_count":63,"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":"2306.09368","title":"Warpformer: A Multi-scale Modeling Approach for Irregular Clinical Time Series","created":"2023-06-14","cited_by_count":55,"area":null},{"id":"2505.11250","title":"Rethinking Irregular Time Series Forecasting: A Simple yet Effective Baseline","created":"2025-05-16","cited_by_count":55,"area":null},{"id":"2106.07875","title":"S-LIME: Stabilized-LIME for Model Explanation","created":"2021-06-15","cited_by_count":52,"area":null},{"id":"2305.10716","title":"A Survey on Time-Series Pre-Trained Models","created":"2023-05-18","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":"2310.02619","title":"Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEs","created":"2023-10-04","cited_by_count":49,"area":null},{"id":"2112.12047","title":"Generating Synthetic Mixed-type Longitudinal Electronic Health Records for Artificial Intelligent Applications","created":"2021-12-22","cited_by_count":47,"area":null},{"id":"2303.16005","title":"Uncovering the Missing Pattern: Unified Framework Towards Trajectory Imputation and Prediction","created":"2023-03-28","cited_by_count":47,"area":null},{"id":"2106.11028","title":"Neural Controlled Differential Equations for Online Prediction Tasks","created":"2021-06-21","cited_by_count":46,"area":null},{"id":"2205.01138","title":"Transformers in Time-series Analysis: A Tutorial","created":"2022-04-28","cited_by_count":46,"area":null},{"id":"2210.02040","title":"GT-GAN: General Purpose Time Series Synthesis with Generative Adversarial Networks","created":"2022-10-05","cited_by_count":44,"area":null},{"id":"2112.06351","title":"Neural Point Process for Learning Spatiotemporal Event Dynamics","created":"2021-12-12","cited_by_count":42,"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":"2206.06243","title":"Contrastive Learning for Unsupervised Domain Adaptation of Time Series","created":"2022-06-13","cited_by_count":36,"area":null},{"id":"2402.14989","title":"Stable Neural Stochastic Differential Equations in Analyzing Irregular Time Series Data","created":"2024-02-22","cited_by_count":36,"area":null},{"id":"2311.14332","title":"GATGPT: A Pre-trained Large Language Model with Graph Attention Network for Spatiotemporal Imputation","created":"2023-11-24","cited_by_count":35,"area":null},{"id":"2107.09951","title":"Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies","created":"2021-07-21","cited_by_count":34,"area":null},{"id":"2210.12156","title":"Improving Medical Predictions by Irregular Multimodal Electronic Health Records Modeling","created":"2022-10-18","cited_by_count":33,"area":null},{"id":"2402.02460","title":"Review of multimodal machine learning approaches in healthcare","created":"2024-02-04","cited_by_count":31,"area":null},{"id":"2102.02340","title":"MUFASA: Multimodal Fusion Architecture Search for Electronic Health Records","created":"2021-02-03","cited_by_count":30,"area":null},{"id":"2103.02164","title":"Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series","created":"2021-03-03","cited_by_count":30,"area":null},{"id":"2302.10902","title":"Deep Imputation of Missing Values in Time Series Health Data: A Review with Benchmarking","created":"2023-02-10","cited_by_count":30,"area":null},{"id":"2401.03717","title":"Universal Time-Series Representation Learning: A Survey","created":"2024-01-08","cited_by_count":30,"area":null},{"id":"2109.01876","title":"Attentive Neural Controlled Differential Equations for Time-series Classification and Forecasting","created":"2021-09-04","cited_by_count":29,"area":null},{"id":"2301.11308","title":"Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series","created":"2023-01-26","cited_by_count":29,"area":null},{"id":"2111.08386","title":"Towards Generating Real-World Time Series Data","created":"2021-11-16","cited_by_count":26,"area":null},{"id":"2102.03340","title":"NRTSI: Non-Recurrent Time Series Imputation","created":"2021-02-05","cited_by_count":25,"area":null},{"id":"2210.08258","title":"Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques","created":"2022-10-15","cited_by_count":25,"area":null},{"id":"2305.02504","title":"Learning Missing Modal Electronic Health Records with Unified Multi-modal Data Embedding and Modality-Aware Attention","created":"2023-05-04","cited_by_count":25,"area":null},{"id":"2208.05836","title":"HyperTime: Implicit Neural Representation for Time Series","created":"2022-08-11","cited_by_count":24,"area":null},{"id":"2304.01429","title":"Learning from data with structured missingness","created":"2023-04-04","cited_by_count":24,"area":null},{"id":"2204.08771","title":"EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecasting","created":"2022-04-19","cited_by_count":22,"area":null},{"id":"2308.06738","title":"Probabilistic Imputation for Time-series Classification with Missing Data","created":"2023-08-13","cited_by_count":22,"area":null},{"id":"2308.16818","title":"Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional Network","created":"2023-08-31","cited_by_count":20,"area":null},{"id":"2310.07402","title":"NuTime: Numerically Multi-Scaled Embedding for Large-Scale Time-Series Pretraining","created":"2023-10-11","cited_by_count":20,"area":null},{"id":"2502.09885","title":"Comprehensive Review of Neural Differential Equations for Time Series Analysis","created":"2025-02-14","cited_by_count":20,"area":null},{"id":"2209.13525","title":"Retrieval Based Time Series Forecasting","created":"2022-09-27","cited_by_count":17,"area":null},{"id":"2304.13017","title":"DuETT: Dual Event Time Transformer for Electronic Health Records","created":"2023-04-25","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":"2206.01649","title":"Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules","created":"2022-06-03","cited_by_count":16,"area":null},{"id":"2105.14953","title":"ACE-NODE: Attentive Co-Evolving Neural Ordinary Differential Equations","created":"2021-05-31","cited_by_count":15,"area":null},{"id":"2306.04255","title":"Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time","created":"2023-06-07","cited_by_count":15,"area":null},{"id":"2410.14030","title":"Graph Neural Flows for Unveiling Systemic Interactions Among Irregularly Sampled Time Series","created":"2024-10-17","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":"2102.04702","title":"AttDMM: An Attentive Deep Markov Model for Risk Scoring in Intensive Care Units","created":"2021-02-09","cited_by_count":14,"area":null},{"id":"2201.08186","title":"Conditional Generation of Medical Time Series for Extrapolation to Underrepresented Populations","created":"2022-01-20","cited_by_count":14,"area":null},{"id":"2203.06852","title":"Continual Learning for Multivariate Time Series Tasks with Variable Input Dimensions","created":"2022-03-14","cited_by_count":14,"area":null},{"id":"2204.09633","title":"SurvLatent ODE : A Neural ODE based time-to-event model with competing risks for longitudinal data improves cancer-associated Venous Thromboembolism (VTE) prediction","created":"2022-04-20","cited_by_count":13,"area":null},{"id":"2402.07594","title":"Zero-shot Imputation with Foundation Inference Models for Dynamical Systems","created":"2024-02-12","cited_by_count":13,"area":null},{"id":"2402.13635","title":"The METRIC-framework for assessing data quality for trustworthy AI in medicine: a systematic review","created":"2024-02-21","cited_by_count":13,"area":null},{"id":"2102.11218","title":"Neural Pharmacodynamic State Space Modeling","created":"2021-02-22","cited_by_count":12,"area":null},{"id":"2310.18688","title":"Clairvoyance: A Pipeline Toolkit for Medical Time Series","created":"2023-10-28","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":"2104.11510","title":"Time Series Forecasting via Learning Convolutionally Low-Rank Models","created":"2021-04-23","cited_by_count":10,"area":null},{"id":"2105.00412","title":"TE-ESN: Time Encoding Echo State Network for Prediction Based on Irregularly Sampled Time Series Data","created":"2021-05-02","cited_by_count":10,"area":null},{"id":"2112.03379","title":"Deep Efficient Continuous Manifold Learning for Time Series Modeling","created":"2021-12-03","cited_by_count":10,"area":null},{"id":"2206.14621","title":"Extracting Weighted Finite Automata from Recurrent Neural Networks for Natural Languages","created":"2022-06-27","cited_by_count":10,"area":null},{"id":"2208.09795","title":"Stop&Hop: Early Classification of Irregular Time Series","created":"2022-08-21","cited_by_count":10,"area":null},{"id":"2405.05959","title":"Self-Supervised Learning of Time Series Representation via Diffusion Process and Imputation-Interpolation-Forecasting Mask","created":"2024-05-09","cited_by_count":10,"area":null},{"id":"2408.08328","title":"Unleashing The Power of Pre-Trained Language Models for Irregularly Sampled Time Series","created":"2024-08-12","cited_by_count":10,"area":null},{"id":"2410.05602","title":"Amortized Control of Continuous State Space Feynman-Kac Model for Irregular Time Series","created":"2024-10-08","cited_by_count":10,"area":null},{"id":"2412.12886","title":"TimeCHEAT: A Channel Harmony Strategy for Irregularly Sampled Multivariate Time Series Analysis","created":"2024-12-17","cited_by_count":10,"area":null},{"id":"2304.11794","title":"FineEHR: Refine Clinical Note Representations to Improve Mortality Prediction","created":"2023-04-24","cited_by_count":9,"area":null},{"id":"2309.04160","title":"PRISM: Mitigating EHR Data Sparsity via Learning from Missing Feature Calibrated Prototype Patient Representations","created":"2023-09-08","cited_by_count":9,"area":null},{"id":"2309.13452","title":"Monotonic Neural Ordinary Differential Equation: Time-series Forecasting for Cumulative Data","created":"2023-09-23","cited_by_count":9,"area":null},{"id":"2409.15167","title":"Data-driven model discovery with Kolmogorov-Arnold networks","created":"2024-09-23","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":"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":"2301.04333","title":"Learnable Path in Neural Controlled Differential Equations","created":"2023-01-11","cited_by_count":8,"area":null},{"id":"2302.00612","title":"Clinical Decision Transformer: Intended Treatment Recommendation through Goal Prompting","created":"2023-02-01","cited_by_count":8,"area":null},{"id":"2305.15111","title":"Reconstruction, forecasting, and stability of chaotic dynamics from partial data","created":"2023-05-24","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":"2405.09039","title":"SMART: Towards Pre-trained Missing-Aware Model for Patient Health Status Prediction","created":"2024-05-15","cited_by_count":8,"area":null},{"id":"2411.15096","title":"RED: Effective Trajectory Representation Learning with Comprehensive Information","created":"2024-11-22","cited_by_count":8,"area":null},{"id":"2505.17488","title":"ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics","created":"2025-05-23","cited_by_count":8,"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":"2508.05287","title":"FlowState: Sampling-Rate-Equivariant Time-Series Forecasting","created":"2025-08-07","cited_by_count":8,"area":null}],"next_cursor":"100"}