Xingwang Li — accepted AI-conference papers

2026

Beyond Uniform Updates: Drift Pattern Aware Online Time Series Forecasting Under Delayed Feedback

Xingwang Li, Fei Teng, Cong Zhou, Qiang Duan

IJCAI 2026

Online time series forecasting relies on continual updates to cope with concept drift. In multi-step forecasting, however, the ground truth for an H-step prediction arrives only after H steps, so a delayed residual entangles persistent drifts with transient shocks and seasonal fluctuations. Existing

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2026

Uncovering Hidden Degeneration: A Physics-Guided Bidirectional Inference Framework for Industrial Time Series Prediction

Xingwang Li, Fei Teng, Xin Wu, Qiang Duan

AAAI 2026technical

Hidden degenerations in industrial time series often precede observable failures, they remain undetected by standard monitoring systems until anomalies become apparent. This gap between microscopic degradation and macroscopic observation renders conventional predictors inherently reactive, as they r

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