Leave a Window Out: Modifying the Jackknife for Predictive Inference in Time Series
Conformal prediction methods enjoy strong theoretical and empirical predictive inference performance, provided the data is exchangeable and is treated symmetrically during training. However, these assumptions are impractical in many settings, such as time series, where temporal dependence violates exchangeability and it is preferable to use predictors that leverage dependence by treating data asymmetrically. Recent work shows that split conformal prediction is robust to these issues, but sample splitting can reduce accuracy, motivating the study of methods that do not rely on data splitting in the time series setting. In this work, we show that the vanilla leave-one-out jackknife can suff...
| Authors | Hanyang Jiang, Rina Foygel Barber, Ashwin Pananjady et al. |
|---|---|
| Primary category | stat.ML |
| Published | 28 May 2026 |
| Last updated at source | 9 June 2026 |
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