{"total":29,"rows":[{"id":"2110.05357","title":"Graph-Guided Network for Irregularly Sampled Multivariate Time Series","created":"2021-10-11","cited_by_count":111,"area":null},{"id":"2306.10125","title":"Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects","created":"2023-06-16","cited_by_count":99,"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":"2309.16940","title":"Asynchrony-Robust Collaborative Perception via Bird's Eye View Flow","created":"2023-09-29","cited_by_count":58,"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":"2402.02032","title":"RobustTSF: Towards Theory and Design of Robust Time Series Forecasting with Anomalies","created":"2024-02-03","cited_by_count":18,"area":null},{"id":"2212.01209","title":"FECAM: Frequency Enhanced Channel Attention Mechanism for Time Series Forecasting","created":"2022-12-02","cited_by_count":15,"area":null},{"id":"2301.02068","title":"Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution","created":"2023-01-05","cited_by_count":15,"area":null},{"id":"2310.20218","title":"A Systematic Review for Transformer-based Long-term Series Forecasting","created":"2023-10-31","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":"2209.00654","title":"Distributional Drift Adaptation with Temporal Conditional Variational Autoencoder for Multivariate Time Series Forecasting","created":"2022-09-01","cited_by_count":7,"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":"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":"2212.10306","title":"Gaussian Process Latent Variable Modeling for Few-shot Time Series Forecasting","created":"2022-12-20","cited_by_count":3,"area":null},{"id":"2402.09434","title":"Disentangling Imperfect: A Wavelet-Infused Multilevel Heterogeneous Network for Human Activity Recognition in Flawed Wearable Sensor Data","created":"2024-01-26","cited_by_count":3,"area":null},{"id":"2108.08170","title":"DeepExpress: Heterogeneous and Coupled Sequence Modeling for Express Delivery Prediction","created":"2021-08-18","cited_by_count":2,"area":null},{"id":"2310.11022","title":"Compatible Transformer for Irregularly Sampled Multivariate Time Series","created":"2023-10-17","cited_by_count":2,"area":null},{"id":"2410.04320","title":"Channel-Aware Throughput Maximization for Cooperative Data Fusion in CAV","created":"2024-10-06","cited_by_count":2,"area":null},{"id":"2509.22121","title":"Mind the Missing: Variable-Aware Representation Learning for Irregular EHR Time Series using Large Language Models","created":"2025-09-26","cited_by_count":2,"area":null},{"id":"2208.08878","title":"Towards Learning in Grey Spatiotemporal Systems: A Prophet to Non-consecutive Spatiotemporal Dynamics","created":"2022-08-17","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":"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":"2312.17286","title":"Comparative study of clustering models for multivariate time series from connected medical devices","created":"2023-12-28","cited_by_count":0,"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":"2505.19193","title":"SuperMAN: Interpretable and Expressive Networks over Temporally Sparse Heterogeneous Data","created":"2025-05-25","cited_by_count":0,"area":null},{"id":"2509.23923","title":"Graph Mixing Additive Networks","created":"2025-09-28","cited_by_count":0,"area":null},{"id":"2602.19531","title":"A Statistical Approach for Modeling Irregular Multivariate Time Series with Missing Observations","created":"2026-02-23","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":"2608.21941","title":"Multimodal Prompt Learning with Irregular EHRs for Robust Monitoring of Critical Care Patients","created":"2026-08-22","cited_by_count":0,"area":null}],"next_cursor":null}