Dynamic Dual-Feedback Conformal Inference for Time Series Forecasting
Quantifying uncertainty in time series forecasting is particularly demanding because sequential data exhibit temporal dependence and are prone to distributional changes. Conformal inference has emerged as a powerful uncertainty quantification approach through the construction of prediction sets. Recent advances have introduced online conformal methods that adaptively adjust prediction thresholds through feedback mechanisms. However, the existing feedback mechanism typically relies solely on miscoverage indicators (actual feedback), whether the true label falls within the interval at each time step, while overlooking the empirical prediction threshold (estimated feedback) that is derived f...
| Authors | Songlin Du, Ling Luo, Uwe Aickelin |
|---|---|
| Primary category | stat.ML |
| Published | 19 October 2026 |
| Last updated at source | 19 October 2026 |
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