Model-Agnostic Online Certificate-Driven Calibration for Time Series Forecasting Under Distribution Shift
Time series out-of-distribution generalization requires forecasters to remain reliable when deployment dynamics differ from training conditions due to covariate shift, concept shift, and temporal dependence. Probably Approximately Correct Bayesian domain adaptation provides computable certificates by decomposing target risk into a source risk term, a source-to-target mismatch term, and a complexity term, but standard analyses rely on independent sampling and distributional stability, assumptions that are violated in time series by serial dependence and nonstationary shift. We propose a model-agnostic online martingale Probably Approximately Correct Bayesian framework that yields finite-sa...
| Authors | Chenfeng Huang, Zixuan Ma, George Michailidis |
|---|---|
| Primary category | cs.LG |
| Published | 25 September 2026 |
| Last updated at source | 3 October 2026 |
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