tsbootstrap: Distribution-Free Uncertainty Quantification and Conformal Prediction for Time Series
Finance, sensing, and demand streams violate the exchangeability that IID conformal prediction and the IID bootstrap assume, and existing libraries implement either a general resampling engine or conformal calibration without the other. tsbootstrap provides block, residual, sieve, and wild resampling, classical bootstrap confidence intervals, and adaptive conformal calibrators (EnbPI, ACI, NexCP, AgACI) through a single typed API in which a specification object selects each method. In a controlled coverage study the IID bootstrap undercovers sharply under dependence; dependence-aware methods reduce the coverage deficit, the sieve nearest to nominal under short-memory linear dependence. On...
| Authors | Sankalp Gilda |
|---|---|
| Primary category | stat.ME |
| Published | 7 July 2026 |
| Last updated at source | 7 July 2026 |
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