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, with 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).
Blogger's Review: The design of tsbootstrap significantly enhances the capability of uncertainty quantification and conformal prediction in time series data analysis, particularly when dealing with dependent data. Its flexible API and efficient resampling methods make this tool highly valuable for practical applications, especially in complex data processing in finance and other fields. The efficiency and adaptability it offers open new possibilities for related research.