3 papers
cs.LG2026
EIDOS: Latent-Space Predictive Learning for Time Series Foundation Models
Xinxing Zhou, Qingren Yao, Yiji Zhao +5
Most time series foundation models are pretrained by directly predicting future observations, which often yields weakly structured latent representations that capture surface noise…
cs.LG2025
Estimating Time Series Foundation Model Transferability via In-Context Learning
Qingren Yao, Ming Jin, Chengqi Zhang +3
Time series foundation models (TSFMs) offer strong zero-shot forecasting via large-scale pre-training, yet fine-tuning remains critical for boosting performance in domains with lim…
cs.LG2024
Towards Neural Scaling Laws for Time Series Foundation Models
Qingren Yao, Chao-Han Huck Yang, Renhe Jiang +3
Scaling laws offer valuable insights into the design of time series foundation models (TSFMs). However, previous research has largely focused on the scaling laws of TSFMs for in-di…