{"total":35,"rows":[{"id":"2510.14510","title":"Enhancing Time Series Forecasting through Selective Representation Spaces: A Patch Perspective","created":"2025-10-16","cited_by_count":39,"area":null},{"id":"2505.12917","title":"Temporal Query Network for Efficient Multivariate Time Series Forecasting","created":"2025-05-19","cited_by_count":31,"area":null},{"id":"2509.22295","title":"Aurora: Towards Universal Generative Multimodal Time Series Forecasting","created":"2025-09-26","cited_by_count":29,"area":null},{"id":"2510.24574","title":"DistDF: Time-Series Forecasting Needs Joint-Distribution Wasserstein Alignment","created":"2025-10-28","cited_by_count":13,"area":null},{"id":"2505.23017","title":"$K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting","created":"2025-05-29","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":"2501.12500","title":"Learning General Causal Structures with Hidden Dynamic Process for Climate Analysis","created":"2025-01-21","cited_by_count":7,"area":null},{"id":"2602.00297","title":"From Observations to States: Latent Time Series Forecasting","created":"2026-01-30","cited_by_count":6,"area":null},{"id":"2508.13773","title":"PENGUIN: Enhancing Transformer with Periodic-Nested Group Attention for Long-term Time Series Forecasting","created":"2025-08-19","cited_by_count":3,"area":null},{"id":"2512.14253","title":"FLAME: Flow Enhanced Legendre Memory Models for General Time Series Forecasting","created":"2025-12-16","cited_by_count":3,"area":null},{"id":"2602.00717","title":"Deep Time-series Forecasting Needs Kernelized Moment Balancing","created":"2026-01-31","cited_by_count":2,"area":null},{"id":"2603.13702","title":"Routing Channel-Patch Dependencies in Time Series Forecasting with Graph Spectral Decomposition","created":"2026-03-14","cited_by_count":2,"area":null},{"id":"2509.23813","title":"IndexNet: Timestamp and Variable-Aware Modeling for Time Series Forecasting","created":"2025-09-28","cited_by_count":1,"area":null},{"id":"2509.25263","title":"How Effective Are Time-Series Models for Precipitation Nowcasting? A Comprehensive Benchmark for GNSS-based Precipitation Nowcasting","created":"2025-09-28","cited_by_count":1,"area":null},{"id":"2601.21681","title":"LLM4Fluid: Large Language Models as Generalizable Neural Solvers for Fluid Dynamics","created":"2026-01-29","cited_by_count":1,"area":null},{"id":"2601.23114","title":"To See Far, Look Close: Evolutionary Forecasting for Long-term Time Series","created":"2026-01-30","cited_by_count":1,"area":null},{"id":"2603.24240","title":"InstanceRSR: Real-World Super-Resolution via Instance-Aware Representation Alignment","created":"2026-03-25","cited_by_count":1,"area":null},{"id":"2603.24262","title":"Forecasting with Guidance: Representation-Level Supervision for Time Series Forecasting","created":"2026-03-25","cited_by_count":1,"area":null},{"id":"2605.02938","title":"PAMNet: Cycle-aware Phase-Amplitude Modulation Network for Multivariate Time Series Forecasting","created":"2026-05-01","cited_by_count":1,"area":null},{"id":"2605.17730","title":"L-Drive: Beyond a Single Mapping-Latent Context Drives Time Series Forecasting","created":"2026-05-18","cited_by_count":1,"area":null},{"id":"2605.21088","title":"Reviving Error Correction in Modern Deep Time-Series Forecasting","created":"2026-05-20","cited_by_count":1,"area":null},{"id":"2606.19412","title":"Spectral Retrieval-Augmented Time-Series Forecasting","created":"2026-06-17","cited_by_count":1,"area":null},{"id":"2608.05742","title":"Multivariate Time Series Forecasting needs Cross Variable Loss","created":"2026-08-06","cited_by_count":1,"area":null},{"id":"2509.25914","title":"ReNF: Rethinking the Design of Neural Long-Term Time Series Forecasters","created":"2025-09-30","cited_by_count":0,"area":null},{"id":"2601.16632","title":"Dual-Prototype Disentanglement: A Context-Aware Enhancement Framework for Time Series Forecasting","created":"2026-01-23","cited_by_count":0,"area":null},{"id":"2603.19899","title":"Deep Time-Series Forecasting in 10 Years: A Survey","created":"2026-03-20","cited_by_count":0,"area":null},{"id":"2604.16428","title":"Non-Stationarity in the Embedding Space of Time Series Foundation Models","created":"2026-04-06","cited_by_count":0,"area":null},{"id":"2605.00466","title":"PAMod: Modeling Cyclical Shifts via Phase-Amplitude Modulation for Non-stationary Time Series Forecasting","created":"2026-05-01","cited_by_count":0,"area":null},{"id":"2605.14551","title":"SeesawNet: Towards Non-stationary Time Series Forecasting with Balanced Modeling of Common and Specific Dependencies","created":"2026-05-14","cited_by_count":0,"area":null},{"id":"2605.16449","title":"PESD-TSF: A Period-Aware and Explicit Structured Decomposition Framework for Long-Term Time Series Forecasting","created":"2026-05-15","cited_by_count":0,"area":null},{"id":"2605.19249","title":"Beyond Extrapolation: Knowledge Utilization Paradigm with Bidirectional Inspiration for Time Series Forecasting","created":"2026-05-19","cited_by_count":0,"area":null},{"id":"2606.06010","title":"Adaptive Oscillatory-State Alignment for Time Series Forecasting","created":"2026-06-04","cited_by_count":0,"area":null},{"id":"2609.06519","title":"Role-Specific Predictive Geometries for Nonstationary Multivariate Graph-Signal Forecasting","created":"2026-09-06","cited_by_count":0,"area":null},{"id":"2609.22820","title":"Beyond Average Error through Oracle-Informed Stress Tests for Time-Series Forecasting","created":"2026-09-19","cited_by_count":0,"area":null},{"id":"2609.34652","title":"Revitalizing Medical Time Series with Vision-Informed Retrieval: A Vision-Language Perspective","created":"2026-09-28","cited_by_count":0,"area":null}],"next_cursor":null}