2 citations · 2 across the 6 of their papers we have counts for
4 papers · 1 filter
MP3: Multi-Period Pattern Pre-training for Spatio-Temporal Forecasting
Lilan Peng, Yandi Liu, Qingren Yao +2
Spatio-Temporal forecasting is crucial in diverse fields, such as transportation, climate, and energy. Urban spatio-temporal data exhibits temporal mirage: similar short-window inp…
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…
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…
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…