{"total":21,"rows":[{"id":"2402.02592","title":"Unified Training of Universal Time Series Forecasting Transformers","created":"2024-02-04","cited_by_count":614,"area":null},{"id":"2410.10393","title":"GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluation","created":"2024-10-14","cited_by_count":212,"area":null},{"id":"2402.02368","title":"Timer: Generative Pre-trained Transformers Are Large Time Series Models","created":"2024-02-04","cited_by_count":208,"area":null},{"id":"2510.15821","title":"Chronos-2: From Univariate to Universal Forecasting","created":"2025-10-17","cited_by_count":194,"area":null},{"id":"2505.14766","title":"This Time is Different: An Observability Perspective on Time Series Foundation Models","created":"2025-05-20","cited_by_count":68,"area":null},{"id":"2402.02370","title":"AutoTimes: Autoregressive Time Series Forecasters via Large Language Models","created":"2024-02-04","cited_by_count":65,"area":null},{"id":"2402.02713","title":"Position: What Can Large Language Models Tell Us about Time Series Analysis","created":"2024-02-05","cited_by_count":47,"area":null},{"id":"2410.12360","title":"Towards Neural Scaling Laws for Time Series Foundation Models","created":"2024-10-16","cited_by_count":33,"area":null},{"id":"2602.12147","title":"It's TIME: Towards the Next Generation of Time Series Forecasting Benchmarks","created":"2026-02-12","cited_by_count":33,"area":null},{"id":"2405.13867","title":"Scaling-laws for Large Time-series Models","created":"2024-05-22","cited_by_count":18,"area":null},{"id":"2503.13868","title":"Out-of-Distribution Generalization in Time Series: A Survey","created":"2025-03-18","cited_by_count":16,"area":null},{"id":"2508.04045","title":"FeDaL: Federated Dataset Learning for General Time Series Foundation Models","created":"2025-08-06","cited_by_count":10,"area":null},{"id":"2510.12681","title":"CoRA: Covariate-Aware Adaptation of Time Series Foundation Models","created":"2025-10-14","cited_by_count":6,"area":null},{"id":"2506.06288","title":"DELPHYNE: A Pre-Trained Model for General and Financial Time Series","created":"2025-05-12","cited_by_count":4,"area":null},{"id":"2404.06198","title":"The impact of data set similarity and diversity on transfer learning success in time series forecasting","created":"2024-04-09","cited_by_count":3,"area":null},{"id":"2504.20099","title":"Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics","created":"2025-04-26","cited_by_count":2,"area":null},{"id":"2604.06727","title":"Bi-level Heterogeneous Learning for Time Series Foundation Models: A Federated Learning Approach","created":"2026-04-08","cited_by_count":2,"area":null},{"id":"2410.18612","title":"TripCast: Pre-training of Masked 2D Transformers for Trip Time Series Forecasting","created":"2024-10-24","cited_by_count":0,"area":null},{"id":"2606.01289","title":"Feature to Dynamics: Feature-space to Autoregression strategy for Zero-shot Time Series Forecasting","created":"2026-05-31","cited_by_count":0,"area":null},{"id":"2606.05264","title":"REGEN: Reference-Guided Synthetic Multivariate Time Series Generation for Forecasting","created":"2026-06-03","cited_by_count":0,"area":null},{"id":"2606.13513","title":"CloudCons: A Comprehensive End-to-End Benchmark for Cloud Resource Consolidation","created":"2026-06-11","cited_by_count":0,"area":null}],"next_cursor":null}