{"total":51,"rows":[{"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":"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":"2507.00191","title":"Beyond Sensor Data: Foundation Models of Behavioral Data from Wearables Improve Health Predictions","created":"2025-06-30","cited_by_count":18,"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":"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":"2411.01842","title":"ElasTST: Towards Robust Varied-Horizon Forecasting with Elastic Time-Series Transformer","created":"2024-11-04","cited_by_count":10,"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":"2308.12551","title":"A Co-training Approach for Noisy Time Series Learning","created":"2023-08-24","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":"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":"2411.00696","title":"CTPD: Cross-Modal Temporal Pattern Discovery for Enhanced Multimodal Electronic Health Records Analysis","created":"2024-11-01","cited_by_count":6,"area":null},{"id":"2508.01971","title":"Revitalizing Canonical Pre-Alignment for Irregular Multivariate Time Series Forecasting","created":"2025-08-04","cited_by_count":6,"area":null},{"id":"2410.12257","title":"Irregularity-Informed Time Series Analysis: Adaptive Modelling of Spatial and Temporal Dynamics","created":"2024-10-16","cited_by_count":4,"area":null},{"id":"2408.04245","title":"Scalable Transformer for High Dimensional Multivariate Time Series Forecasting","created":"2024-08-08","cited_by_count":3,"area":null},{"id":"2406.12738","title":"Large Language Model as a Universal Clinical Multi-task Decoder","created":"2024-06-18","cited_by_count":2,"area":null},{"id":"2505.22815","title":"IMTS is Worth Time $\\times$ Channel Patches: Visual Masked Autoencoders for Irregular Multivariate Time Series Prediction","created":"2025-05-28","cited_by_count":2,"area":null},{"id":"2505.23475","title":"TimePoint: Accelerated Time Series Alignment via Self-Supervised Keypoint and Descriptor Learning","created":"2025-05-29","cited_by_count":2,"area":null},{"id":"2509.17809","title":"MTM: A Multi-Scale Token Mixing Transformer for Irregular Multivariate Time Series Classification","created":"2025-09-22","cited_by_count":2,"area":null},{"id":"2511.21561","title":"Machine Learning Approaches to Clinical Risk Prediction: Multi-Scale Temporal Alignment in Electronic Health Records","created":"2025-11-26","cited_by_count":2,"area":null},{"id":"2605.13711","title":"MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling","created":"2026-05-13","cited_by_count":2,"area":null},{"id":"2311.07744","title":"Two-Stage Aggregation with Dynamic Local Attention for Irregular Time Series","created":"2023-11-13","cited_by_count":1,"area":null},{"id":"2408.16261","title":"Evaluating Time-Series Training Dataset through Lens of Spectrum in Deep State Space Models","created":"2024-08-29","cited_by_count":1,"area":null},{"id":"2412.10621","title":"WaveGNN: Integrating Graph Neural Networks and Transformers for Decay-Aware Classification of Irregular Clinical Time-Series","created":"2024-12-14","cited_by_count":1,"area":null},{"id":"2502.06134","title":"Integrating Sequence and Image Modeling in Irregular Medical Time Series Through Self-Supervised Learning","created":"2025-02-10","cited_by_count":1,"area":null},{"id":"2502.11816","title":"Mixing It Up: Exploring Mixer Networks for Irregular Multivariate Time Series Forecasting","created":"2025-02-17","cited_by_count":1,"area":null},{"id":"2506.08660","title":"Towards Robust Real-World Multivariate Time Series Forecasting: A Unified Framework for Dependency, Asynchrony, and Missingness","created":"2025-06-10","cited_by_count":1,"area":null},{"id":"2511.06854","title":"Beyond Observations: Reconstruction Error-Guided Irregularly Sampled Time Series Representation Learning","created":"2025-11-10","cited_by_count":1,"area":null},{"id":"2603.05997","title":"MM-ISTS: Cooperating Irregularly Sampled Time Series Forecasting with Multimodal Vision-Text LLMs","created":"2026-03-06","cited_by_count":1,"area":null},{"id":"2605.10179","title":"One-Step Graph-Structured Neural Flows for Irregular Multivariate Time Series Classification","created":"2026-05-11","cited_by_count":1,"area":null},{"id":"2607.28035","title":"Enhancing Irregular Time Series Forecasting with Continuous-Time Modeling Framework","created":"2026-07-30","cited_by_count":1,"area":null},{"id":"2412.01063","title":"MuSiCNet: A Gradual Coarse-to-Fine Framework for Irregularly Sampled Multivariate Time Series Analysis","created":"2024-12-02","cited_by_count":0,"area":null},{"id":"2505.00590","title":"Unlocking the Potential of Linear Networks for Irregular Multivariate Time Series Forecasting","created":"2025-05-01","cited_by_count":0,"area":null},{"id":"2505.04167","title":"STRGCN: Capturing Asynchronous Spatio-Temporal Dependencies for Irregular Multivariate Time Series Forecasting","created":"2025-05-07","cited_by_count":0,"area":null},{"id":"2602.02230","title":"SEDformer: Event-Synchronous Spiking Transformers for Irregular Telemetry Time Series Forecasting","created":"2026-02-02","cited_by_count":0,"area":null},{"id":"2602.21498","title":"Learning Recursive Multi-Scale Representations for Irregular Multivariate Time Series Forecasting","created":"2026-02-25","cited_by_count":0,"area":null},{"id":"2604.04614","title":"A Clinical Point Cloud Paradigm for In-Hospital Mortality Prediction from Multi-Level Incomplete Multimodal EHRs","created":"2026-04-06","cited_by_count":0,"area":null},{"id":"2604.11842","title":"DBGL: Decay-aware Bipartite Graph Learning for Irregular Medical Time Series Classification","created":"2026-04-12","cited_by_count":0,"area":null},{"id":"2604.27967","title":"Differentiable latent structure discovery for interpretable forecasting in clinical time series","created":"2026-04-30","cited_by_count":0,"area":null},{"id":"2605.19805","title":"Latent Laplace Diffusion for Irregular Multivariate Time Series","created":"2026-05-19","cited_by_count":0,"area":null},{"id":"2605.27044","title":"BatteryMFormer: Multi-level Learning for Battery Degradation Trajectory Forecasting","created":"2026-05-26","cited_by_count":0,"area":null},{"id":"2605.28166","title":"QuITE: Query-Based Irregular Time Series Embedding","created":"2026-05-27","cited_by_count":0,"area":null},{"id":"2605.28603","title":"Online Irregular Multivariate Time Series Forecasting via Uncertainty-Driven Dual-Expert Calibration","created":"2026-05-27","cited_by_count":0,"area":null},{"id":"2606.21885","title":"Cohort-Anchored Foundation Models for Electronic Health Records: From Risk Scores to Auditable Peer Cohorts","created":"2026-06-20","cited_by_count":0,"area":null},{"id":"2607.21922","title":"MissHyper: Restoring Clinical Synchronicity in Missingness-Guided Hypergraph Forecasting","created":"2026-07-24","cited_by_count":0,"area":null},{"id":"2608.10969","title":"GARLIC: Graph Attention-based Relational Learning of Multivariate Time Series in Intensive Care","created":"2026-08-11","cited_by_count":0,"area":null},{"id":"2608.17284","title":"Rethinking Irregular Time Series Forecasting from the Perspective of Basis Functions","created":"2026-08-18","cited_by_count":0,"area":null},{"id":"2608.17293","title":"Beyond MSE: Rethinking the Evaluation Metric and Benchmarking for Irregular Time Series Forecasting","created":"2026-08-18","cited_by_count":0,"area":null},{"id":"2608.19171","title":"L\\'evy Attention: Single-Pass Predictive Uncertainty for Continuous-Time Attention","created":"2026-08-19","cited_by_count":0,"area":null},{"id":"2608.29640","title":"LLMODE: Aligning ODEs with LLMs via Gated Token Injection for Irregular Spatio-Temporal Forecasting","created":"2026-08-30","cited_by_count":0,"area":null},{"id":"2609.33303","title":"BITS: Rethinking Fair and Comprehensive Evaluation for Irregular Time Series Forecasting","created":"2026-09-27","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}],"next_cursor":null}