{"total":16,"rows":[{"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":"2603.08032","title":"GCGNet: Graph-Consistent Generative Network for Time Series Forecasting with Exogenous Variables","created":"2026-03-09","cited_by_count":13,"area":null},{"id":"2510.04910","title":"Glocal Information Bottleneck for Time Series Imputation","created":"2025-10-06","cited_by_count":9,"area":null},{"id":"2509.23494","title":"Revisiting Multivariate Time Series Forecasting with Missing Values","created":"2025-09-27","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":"2601.21135","title":"TRACE: Trajectory Recovery for Continuous Mechanism Evolution in Causal Representation Learning","created":"2026-01-29","cited_by_count":4,"area":null},{"id":"2602.00654","title":"PHAT: Modeling Period Heterogeneity for Multivariate Time Series Forecasting","created":"2026-01-31","cited_by_count":3,"area":null},{"id":"2608.13262","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","created":"2026-08-13","cited_by_count":3,"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":"2606.13571","title":"Existence Precedes Value: Joint Modeling of Observational Existence and Evolving States in Time Series Forecasting","created":"2026-06-11","cited_by_count":1,"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":"2607.09537","title":"GatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series Forecasting","created":"2026-07-10","cited_by_count":0,"area":null},{"id":"2609.27277","title":"TimeEvo: Failure-Driven Self-Evolution of a Time Series Agent","created":"2026-09-23","cited_by_count":0,"area":null},{"id":"2609.34939","title":"XMatch: Enhancing Covariate-Aware Time Series Forecasting through Tree-Structured Exogenous Matching","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2610.19831","title":"CTIA-ODE: Continuous-Time Inter-Variable Attention for Long-Term Time Series Forecasting","created":"2026-10-14","cited_by_count":0,"area":null}],"next_cursor":null}