2 papers
cs.LG2026
The Forecast After the Forecast: A Post-Processing Shift in Time Series
Daojun Liang, Qi Li, Yinglong Wang +5
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 forec…
cs.LG2026
DeepBooTS: Dual-Stream Residual Boosting for Drift-Resilient Time-Series Forecasting
Daojun Liang, Jing Chen, Xiao Wang +2
Time-Series (TS) exhibits pronounced non-stationarity. Consequently, most forecasting methods display compromised robustness to concept drift, despite the prevalent application of…