Author

Changjian Chen

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#machine learning Preprint Sep 2026

STCFormer: Adaptive Spatio-Temporal Modeling with Dynamic Cluster Transformer for Station-based Weather Forecasting

Station-based weather forecasting supports daily life and economic activity, yet accurate forecasts require modeling complex spatial dependencies among stations. Recent clustering-based selective modeling offers a promising alternative to dense inter-station interactions. However, a grouping shared across an observatio...

Rong Li, Hai-Xin Xie, Ming-Yang Wang et al. · 0 citations

Preprint Aug 2026

GLAIM: Learning Global and Local Adaptive Inter-Variable Dependency for Multivariate Time Series Imputation

GLAIM is a Global-Local Adaptive Inter-variable Dependency Modeling framework for multivariate time series imputation that achieves state-of-the-art performance under random and block missingness, remains robust to missing-rate shifts, and benefits from its complementary global and local components.

Ming-Yang Wang, Rong Li, Xiao Wang et al. · 0 citations

#artificial intelligence Preprint Aug 2026

Beyond MSE: Rethinking the Evaluation Metric and Benchmarking for Irregular Time Series Forecasting

The Continuous-time Squared Error (CSE) is proposed, which employs importance weighting to eliminate the influence of the timestamp sampling distributions and theoretically proves that CSE's asymptotic estimation error with respect to continuous-time risk is no greater than that of MSE.

Rong Li, Hai-Xin Xie, Xiao Wang et al. · 0 citations

#artificial intelligence Preprint Aug 2026

Rethinking Irregular Time Series Forecasting from the Perspective of Basis Functions

Irregular time series forecasting is crucial in many domains, such as healthcare and meteorological observation. However, due to the inherent characteristics of irregular time series, including sparse observations and non-uniform sampling, accurately predicting future dynamics remains challenging. In light of these two...

Rong Li, Changjian Chen · 0 citations