{"ID":6138843,"CreatedAt":"2026-07-09T01:07:32.349475501Z","UpdatedAt":"2026-07-10T17:57:37.5932979Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.06690","arxiv_id":"2607.06690","title":"tsbootstrap: Distribution-Free Uncertainty Quantification and Conformal Prediction for Time Series","abstract":"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 the shared fixed-statistic path a compiled backend runs several times faster than arch, and a streaming reduce avoids materializing the $O(Bn)$ replicate tensor, limiting peak extra memory to $O(B)$ for the statistic array. The software is MIT licensed (v0.6.1).","short_abstract":"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 bootstr...","url_abs":"https://arxiv.org/abs/2607.06690","url_pdf":"https://arxiv.org/pdf/2607.06690v1","authors":"[\"Sankalp Gilda\"]","published":"2026-07-07T18:07:06Z","proceeding":"stat.ME","tasks":"[\"stat.ME\",\"cs.AI\",\"cs.MS\",\"q-fin.ST\",\"stat.AP\"]","methods":"[]","has_code":false}