10 papers
Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis
Yisong Fu, Zezhi Shao, Chengqing Yu +4
We present Zeus, a unified tuning-free Time Series Foundation Model (TSFM) that delivers superior performance across diverse analysis tasks without any task-specific fine-tuning. U…
Teacher-Guided Student Self-Knowledge Distillation Using Diffusion Model
Yu Wang, Chuanguang Yang, Zhulin An +6
Existing Knowledge Distillation (KD) methods often align feature information between teacher and student by exploring meaningful feature processing and loss functions. However, due…
APT: Affine Prototype-Timestamp For Time Series Forecasting Under Distribution Shift
Yujie Li, Zezhi Shao, Chengqing Yu +4
Time series forecasting under distribution shift remains challenging, as existing deep learning models often rely on local statistical normalization (e.g., mean and variance) that…
STA-GANN: A Valid and Generalizable Spatio-Temporal Kriging Approach
Yujie Li, Zezhi Shao, Chengqing Yu +6
Spatio-temporal tasks often encounter incomplete data arising from missing or inaccessible sensors, making spatio-temporal kriging crucial for inferring the completely missing temp…
Selective Learning for Deep Time Series Forecasting
Yisong Fu, Zezhi Shao, Chengqing Yu +5
Benefiting from high capacity for capturing complex temporal patterns, deep learning (DL) has significantly advanced time series forecasting (TSF). However, deep models tend to suf…
On the Integration of Spatial-Temporal Knowledge: A Lightweight Approach to Atmospheric Time Series Forecasting
Yisong Fu, Fei Wang, Zezhi Shao +6
Transformers have gained attention in atmospheric time series forecasting (ATSF) for their ability to capture global spatial-temporal correlations. However, their complex architect…