为时间序列设计无需分布假设的不确定性量化方法，解决传统方法在依赖数据下的失效问题。
tsbootstrap: Distribution-Free Uncertainty Quantification and Conformal Prediction for Time Series
- 采用块抽样、残差抽样等四类时间序列专用重采样技术，适配依赖结构
- 在短记忆线性依赖下，筛法（sieve）最接近名义覆盖率，显著优于独立同分布抽样
- 支持多种校准器，适合金融、传感等依赖性强的时间序列场景
金融、传感和需求流等时间序列违反了独立同分布（IID）假设，现有工具要么仅提供通用重采样，要么仅支持合规校准。tsbootstrap通过单一类型化API集成块抽样、残差抽样、筛法和野生抽样，以及经典自助法置信区间与自适应合规校准器（EnbPI、ACI、NexCP、AgACI）。在控制覆盖率实验中，独立同分布自助法在依赖条件下严重低估；依赖感知方法显著减少覆盖率偏差，筛法在短记忆线性依赖下最接近名义水平。在共享固定统计量路径上，编译后端运行速度比arch快数倍，流式归约避免生成$O(Bn)$的复制张量，峰值额外内存仅需$O(B)$的统计量数组。软件采用MIT许可证（v0.6.1）。
原文摘要 · Abstract (English)
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).
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。