TRACE proposes a temporal conditional estimation paradigm for multimodal time series foundation models that infers incomplete target modalities from auxiliary ones, outperforming prior fusion methods on clinical and sentiment benchmarks under missingness.
Empowering time series analysis with synthetic data: A survey and outlook in the era of foundation models.arXiv preprint arXiv:2503.11411,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
baseline 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
baseline 1polarities
baseline 1representative citing papers
TimeRFT applies reinforcement learning with multi-faceted step-wise rewards and informative sample selection to improve generalization and accuracy in TSFM adaptation beyond supervised fine-tuning.
citing papers explorer
-
TimeRFT: Stimulating Generalizable Time Series Forecasting for TSFMs via Reinforcement Finetuning
TimeRFT applies reinforcement learning with multi-faceted step-wise rewards and informative sample selection to improve generalization and accuracy in TSFM adaptation beyond supervised fine-tuning.