{"total":33,"rows":[{"id":"2406.16964","title":"Are Language Models Actually Useful for Time Series Forecasting?","created":"2024-06-22","cited_by_count":168,"area":null},{"id":"2403.00131","title":"UniTS: A Unified Multi-Task Time Series Model","created":"2024-02-29","cited_by_count":94,"area":null},{"id":"2401.17548","title":"Rethinking Channel Dependence for Multivariate Time Series Forecasting: Learning from Leading Indicators","created":"2024-01-31","cited_by_count":32,"area":null},{"id":"2405.17478","title":"Towards a General Time Series Forecasting Model with Unified Representation and Adaptive Transfer","created":"2024-05-24","cited_by_count":32,"area":null},{"id":"2401.03717","title":"Universal Time-Series Representation Learning: A Survey","created":"2024-01-08","cited_by_count":30,"area":null},{"id":"2402.04852","title":"Multi-Patch Prediction: Adapting LLMs for Time Series Representation Learning","created":"2024-02-07","cited_by_count":25,"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":"2510.04134","title":"PhaseFormer: From Patches to Phases for Efficient and Effective Time Series Forecasting","created":"2025-10-05","cited_by_count":9,"area":null},{"id":"2406.07438","title":"DeformTime: Capturing Variable Dependencies with Deformable Attention for Time Series Forecasting","created":"2024-06-11","cited_by_count":7,"area":null},{"id":"2502.14704","title":"Not All Data are Good Labels: On the Self-supervised Labeling for Time Series Forecasting","created":"2025-02-20","cited_by_count":4,"area":null},{"id":"2410.06651","title":"Toward Physics-guided Time Series Embedding","created":"2024-10-09","cited_by_count":3,"area":null},{"id":"2410.23222","title":"Dataset-Driven Channel Masks in Transformers for Multivariate Time Series","created":"2024-10-30","cited_by_count":3,"area":null},{"id":"2503.21834","title":"A Multi-Modal Knowledge-Enhanced Framework for Vessel Trajectory Prediction","created":"2025-03-27","cited_by_count":3,"area":null},{"id":"2509.20942","title":"Why Attention Fails: The Degeneration of Transformers into MLPs in Time Series Forecasting","created":"2025-09-25","cited_by_count":3,"area":null},{"id":"2609.04239","title":"EXAONE Finance 1.0: An Attention-free Time Series Foundation Model for Financial Time Series","created":"2026-08-04","cited_by_count":3,"area":null},{"id":"2408.09703","title":"Partial-Multivariate Model for Forecasting","created":"2024-08-19","cited_by_count":2,"area":null},{"id":"2412.05498","title":"A New Perspective on Time Series Anomaly Detection: Faster Patch-based Broad Learning System","created":"2024-12-07","cited_by_count":2,"area":null},{"id":"2412.13607","title":"PreMixer: MLP-Based Pre-training Enhanced MLP-Mixers for Large-scale Traffic Forecasting","created":"2024-12-18","cited_by_count":2,"area":null},{"id":"2504.00120","title":"EMForecaster: A Deep Learning Framework for Time Series Forecasting in Wireless Networks with Distribution-Free Uncertainty Quantification","created":"2025-03-31","cited_by_count":2,"area":null},{"id":"2504.10900","title":"Bridging Distribution Gaps in Time Series Foundation Model Pretraining with Prototype-Guided Normalization","created":"2025-04-15","cited_by_count":2,"area":null},{"id":"2509.26157","title":"Entropy Guided Dynamic Patch Segmentation for Time Series Transformers","created":"2025-09-30","cited_by_count":2,"area":null},{"id":"2604.22780","title":"A General Framework for Generative Self-supervised Learning in Non-invasive Estimation of Physiological Parameters Using Photoplethysmography","created":"2026-04-03","cited_by_count":2,"area":null},{"id":"2504.18878","title":"TSRM: A Lightweight Temporal Feature Encoding Architecture for Time Series Forecasting and Imputation","created":"2025-04-26","cited_by_count":1,"area":null},{"id":"2512.22550","title":"TimePerceiver: An Encoder-Decoder Framework for Generalized Time-Series Forecasting","created":"2025-12-27","cited_by_count":1,"area":null},{"id":"2506.09174","title":"Multivariate Long-term Time Series Forecasting with Fourier Neural Filter","created":"2025-06-10","cited_by_count":0,"area":null},{"id":"2507.05891","title":"Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection","created":"2025-07-08","cited_by_count":0,"area":null},{"id":"2509.14642","title":"DeCoP: Enhancing Self-Supervised Time Series Representation with Dependency Controlled Pre-training","created":"2025-09-18","cited_by_count":0,"area":null},{"id":"2510.05172","title":"Learning More with Less: A Generalizable, Self-Supervised Framework for Privacy-Preserving Capacity Estimation with EV Charging Data","created":"2025-10-05","cited_by_count":0,"area":null},{"id":"2512.12301","title":"TwinFormer: A Dual-Level Transformer for Long-Sequence Time-Series Forecasting","created":"2025-12-13","cited_by_count":0,"area":null},{"id":"2609.12639","title":"Explaining Time Series Forecasting with Horizon-Resolved Attribution","created":"2026-09-11","cited_by_count":0,"area":null},{"id":"2609.30880","title":"EXAONE Demand 1.0: A Time Series Foundation Model for Demand Forecasting","created":"2026-09-25","cited_by_count":0,"area":null},{"id":"2609.35021","title":"Addressing Spatial Indistinguishability in Spatiotemporal Prediction via Optimal Transport-Guided Masking","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2609.38197","title":"DualCast: A Dual-Path Language Model for Bimodal Financial Time-Series Forecasting","created":"2026-09-18","cited_by_count":0,"area":null}],"next_cursor":null}