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

2026-05-27

AI Summary

This work addresses the performance degradation in online forecasting of irregular multivariate time series caused by dynamically shifting data distributions. To tackle this challenge, the authors propose Under-Cali, a lightweight and model-agnostic online calibration framework that operates without updating the frozen source model. Under-Cali leverages uncertainty estimation as a core control signal and employs a dual-expert calibration mechanism coupled with an adaptive routing strategy to differentially process and jointly update samples with high versus low uncertainty. Evaluated across multiple benchmark datasets, the framework consistently achieves stable performance improvements while maintaining low computational overhead.

Abstract

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. Since irregular sampling fundamentally undermines temporal continuity and periodicity, we cannot leverage these widely studied characteristics from regular MTS for online learning. To this end, we study the problem of online IMTS forecasting and propose Under-Cali, an uncertainty-driven dual-expert calibration framework consisting of three core components: an uncertainty estimator, a dual-expert calibration module, and an adaptive routing module. We design an uncertainty estimator that serves as the core control signal to jointly manage inference and adaptation processes. In our framework, the uncertainty estimator first assesses uncertainty for each incoming batch. The adaptive routing module then directs samples with high uncertainty to the unreliable expert for calibration, while low uncertainty samples remain with the reliable expert. Subsequently, the system updates the reliable expert and the uncertainty estimator using well-calibrated reliable samples, and updates the unreliable expert with challenging samples, enabling stable and efficient online learning. Under-Cali keeps the source forecasting model frozen and performs adaptation only through a lightweight, model-agnostic calibration module, enabling efficient adaptation. Extensive experiments on IMTS benchmarks demonstrate consistent improvements with low computational cost. Our code is available at https://github.com/HaonanWen/Under-Cali.

Problem

• irregular multivariate time series
• online forecasting
• distribution shift
• dynamic missingness
• temporal irregularity

Innovation

• uncertainty estimation
• dual-expert calibration
• online adaptation
• irregular multivariate time series
• model-agnostic calibration