Online Irregular Multivariate Time Series Forecasting via Uncertainty-Driven Dual-Expert Calibration

ArXi:2605.28603v1 Announce Type: cross Irregular multivariate time series forecasting is critical in many real-world applications, where time series are irregularly sampled and exhibit dynamically evolving missingness patterns. Although existing methods perform well in offline settings, they often suffer from significant performance degradation when deployed online due to dynamic shifts in data distribution. Maintaining forecasting capability in such dynamic scenarios typically necessitates online adaptation techniques.