5 papers
WxFM-XL: Adapting Univariate Foundation Models to Multi-Station Weather Forecasting
Xiao Wang, Changjian Chen, Zhuo Tang +3
With the rise of univariate time series foundation models (e.g., Sundial, Timer), initial efforts have been made to extend them to multivariate settings. However, these models main…
StationPDE: Station-Oriented Surface PDE Learning for Multi-Station Multivariate Weather Forecasting
Xiao Wang, Changjian Chen, Rongwen Li +3
Multi-station multivariate weather forecasting aims to forecast future weather variables at multiple weather stations from historical surface observations. Existing station forecas…
Beyond MSE: Rethinking the Evaluation Metric and Benchmarking for Irregular Time Series Forecasting
Rongwen Li, Haixin Xie, Xiao Wang +1
Existing research on irregular time-series forecasting has primarily focused on model design, while evaluation metrics remain insufficiently studied. Existing benchmarks typically…
Rethinking Irregular Time Series Forecasting from the Perspective of Basis Functions
Rongwen Li, Changjian Chen
Irregular time series forecasting is crucial in many domains, such as healthcare and meteorological observation. However, due to the inherent characteristics of irregular time seri…
GLAIM: Learning Global and Local Adaptive Inter-Variable Dependency for Multivariate Time Series Imputation
Mingyang Wang, Rongwen Li, Xiao Wang +1
Multivariate time series imputation is fundamental to downstream analysis, yet modeling inter-variable dependencies with incomplete observations remains challenging. Existing metho…