{"total":37,"rows":[{"id":"2409.18479","title":"CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patterns","created":"2024-09-27","cited_by_count":106,"area":null},{"id":"2405.15124","title":"Scaling Law for Time Series Forecasting","created":"2024-05-24","cited_by_count":33,"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":"2505.17847","title":"Time-o1: Time-Series Forecasting Needs Transformed Label Alignment","created":"2025-05-23","cited_by_count":22,"area":null},{"id":"2405.13522","title":"Intervention-Aware Forecasting: Breaking Historical Limits from a System Perspective","created":"2024-05-22","cited_by_count":17,"area":null},{"id":"2502.12920","title":"Lightweight Online Adaption for Time Series Foundation Model Forecasts","created":"2025-02-18","cited_by_count":16,"area":null},{"id":"2506.23424","title":"Accurate Parameter-Efficient Test-Time Adaptation for Time Series Forecasting","created":"2025-06-29","cited_by_count":14,"area":null},{"id":"2502.12944","title":"Performance of Zero-Shot Time Series Foundation Models on Cloud Data","created":"2025-02-18","cited_by_count":10,"area":null},{"id":"2410.12326","title":"Understanding Why Large Language Models Can Be Ineffective in Time Series Analysis: The Impact of Modality Alignment","created":"2024-10-16","cited_by_count":7,"area":null},{"id":"2506.14957","title":"POCO: Scalable Neural Forecasting through Population Conditioning","created":"2025-06-17","cited_by_count":7,"area":null},{"id":"2407.15247","title":"TimeInf: Time Series Data Contribution via Influence Functions","created":"2024-07-21","cited_by_count":6,"area":null},{"id":"2502.16890","title":"ReFocus: Reinforcing Mid-Frequency and Key-Frequency Modeling for Multivariate Time Series Forecasting","created":"2025-02-24","cited_by_count":6,"area":null},{"id":"2605.17250","title":"Towards Principled Test-Time Adaptation for Time Series Forecasting","created":"2026-05-17","cited_by_count":5,"area":null},{"id":"2405.15256","title":"FTMixer: Frequency and Time Domain Representations Fusion for Time Series Modeling","created":"2024-05-24","cited_by_count":4,"area":null},{"id":"2410.10502","title":"A Practical Approach to Causal Inference over Time","created":"2024-10-14","cited_by_count":4,"area":null},{"id":"2411.17257","title":"Disentangled Parameter-Efficient Linear Model for Long-Term Time Series Forecasting","created":"2024-11-26","cited_by_count":4,"area":null},{"id":"2503.12930","title":"Augmented Invertible Koopman Autoencoder for long-term time series forecasting","created":"2025-03-17","cited_by_count":4,"area":null},{"id":"2509.02341","title":"RDIT: Residual-based Diffusion Implicit Models for Probabilistic Time Series Forecasting","created":"2025-09-02","cited_by_count":4,"area":null},{"id":"2410.17159","title":"LiNo: Advancing Recursive Residual Decomposition of Linear and Nonlinear Patterns for Robust Time Series Forecasting","created":"2024-10-22","cited_by_count":3,"area":null},{"id":"2412.20170","title":"Real-time Calibration Model for Low-cost Sensor in Fine-grained Time series","created":"2024-12-28","cited_by_count":2,"area":null},{"id":"2511.12791","title":"Optimal Look-back Horizon for Time Series Forecasting in Federated Learning","created":"2025-11-16","cited_by_count":2,"area":null},{"id":"2603.02840","title":"Adapting Time Series Foundation Models through Data Mixtures","created":"2026-03-03","cited_by_count":2,"area":null},{"id":"2510.08657","title":"Inner-Instance Normalization for Time Series Forecasting","created":"2025-10-09","cited_by_count":1,"area":null},{"id":"2606.27282","title":"How Good Can Linear Models Be for Time-Series Forecasting?","created":"2026-06-25","cited_by_count":1,"area":null},{"id":"2503.17214","title":"ML-Based Bidding Price Prediction for Pay-As-Bid Ancillary Services Markets: A Use Case in the German Control Reserve Market","created":"2025-03-21","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":"2509.19471","title":"Transformer Modeling for Both Scalability and Performance in Multivariate Time Series","created":"2025-09-23","cited_by_count":0,"area":null},{"id":"2509.24122","title":"Echo Flow Networks","created":"2025-09-28","cited_by_count":0,"area":null},{"id":"2510.08661","title":"CATS-Linear: Classification Auxiliary Linear Model for Time Series Forecasting","created":"2025-10-09","cited_by_count":0,"area":null},{"id":"2512.00925","title":"D-CTNet: A Dual-Branch Channel-Temporal Forecasting Network with Frequency-Domain Correction","created":"2025-11-30","cited_by_count":0,"area":null},{"id":"2512.22702","title":"Position: Current Benchmarking Hinders Real Progress in Deep Learning for Time Series Forecasting","created":"2025-12-27","cited_by_count":0,"area":null},{"id":"2602.00672","title":"Strong Linear Baselines Strike Back: Closed-Form Linear Models as Gaussian Process Conditional Density Estimators for TSAD","created":"2026-01-31","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":"2605.08005","title":"STEPS: A Temporal Smooth Error Propagation Solver on the Manifolds for Test-Time Adaptation in Time Series Forecasting","created":"2026-05-08","cited_by_count":0,"area":null},{"id":"2609.33984","title":"From HL to H+L-1 Parameters: A Hankel-Toeplitz Forecaster for Long-Term Time Series Forecasting","created":"2026-09-27","cited_by_count":0,"area":null},{"id":"2609.34638","title":"Correction-space Cross-variate Interaction for Test-time Adaptation in Time Series Forecasting","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2610.14376","title":"DPAL: A Test-Time Adaptation Loss for Financial Time Series Forecasting","created":"2026-10-09","cited_by_count":0,"area":null}],"next_cursor":null}