← Search

Daojun Liang

2 accepted papers

2026

DeepBooTS: Dual-Stream Residual Boosting for Drift-Resilient Time-Series Forecasting

AAAI 2026technical

Time-Series (TS) exhibits pronounced non-stationarity. Consequently, most forecasting methods display compromised robustness to concept drift, despite the prevalent application of instance normalization. We tackle this challenge by first analysing concept drift through a bias-variance lens and provi

Cited by 0SourcePDFScholar
2026

The Forecast After the Forecast: A Post-Processing Shift in Time Series

ICLR 2026poster

Time series forecasting has long been dominated by advances in model architecture, with recent progress driven by deep learning and hybrid statistical techniques. However, as forecasting models approach diminishing returns in accuracy, a critical yet underexplored opportunity emerges: the strategic…

Cited by 0SourcecodeScholar