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tsbootstrap: Distribution-Free Uncertainty Quantification and Conformal Prediction for Time Series

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  • Sankalp Gilda

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).

Suggested Citation

  • Sankalp Gilda, 2026. "tsbootstrap: Distribution-Free Uncertainty Quantification and Conformal Prediction for Time Series," Papers 2607.06690, arXiv.org.
    • Handle: RePEc:arx:papers:2607.06690
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    References listed on IDEAS

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    3. Shao, Xiaofeng, 2010. "The Dependent Wild Bootstrap," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 218-235.
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