Probabilistic forecasters are increasingly learned, yet the baselines they are compared against are often weak or omitted. We show that the simplest possible conformal interval - a last-value point forecast wrapped in a finite-sample split-conformal residual quantile, with no parameters and no training - is a far stronger baseline than its near-total absence from recent learned-forecasting and conformal-time-series comparisons would suggest. In one-step-ahead online forecasting across 2,217 real series from nine public sources (Monash, LOTSA, the LTSF traffic/electricity/weather suites, METR-LA, BOOM, nips/probts), this ConformalNaive interval decisively beats the naive value-quantile baselines, the entire NPTS family (NPTS 73%, SeasonalNPTS 64% of series), and the published Conformal Seasonal Pools (CSP) method (71% of series, bootstrap 95% CI [69,73], paired Wilcoxon p approx 7.6e-135); it is on par with the simpler learned conformal predictors (RCI, quantile regression; median relative Winkler within 2%) and is beaten only by the adaptive-online and ensemble methods (SPCI, ACI, AgACI), which track distribution shift and lead by 9-33% relative Winkler. It is also better calibrated than a trained neural forecaster: on the six datasets that introduced DeepNPTS, the trivial floors cover the truth 84-85% of the time at a nominal 95%, versus DeepNPTS's 66%. At multi-step seasonal horizons the picture inverts: the random-walk floor is the weakest method and the seasonal pool (CSP) wins - a boundary we map. Finally we give ConformalNaive+, a one-line, training-free, horizon-adaptive selector that attains the better of two complementary floors at every horizon with restored coverage. We argue the matching conformal naive floor must be a mandatory baseline whenever a learned probabilistic forecaster claims gains.

翻译：概率预测器越来越多地依赖学习模型，但与之对比的基线往往薄弱或缺失。我们证明，最简单的共形区间——一个基于有限样本分割共形残差分位数的末值点预测（无参数、无训练）——作为基线，其威力远超近年学习型预测与共形时间序列比较中几乎完全忽略这一方法所暗示的程度。在来自九个公开来源（Monash、LOTSA、LTSF交通/电力/天气套件、METR-LA、BOOM、nips/probts）的2,217个真实序列的单步在线预测中，该ConformalNaive区间显著优于朴素值分位数基线、整个NPTS家族（NPTS在73%序列上占优，SeasonalNPTS在64%序列上占优）以及已发表的共形季节池（CSP）方法（在71%序列上占优，bootstrap 95%置信区间[69,73]，配对Wilcoxon检验p值约7.6e-135）；它与更简单的学习型共形预测器（RCI、分位数回归；中位相对Winkler分数差异在2%以内）性能相当，仅输给自适应在线方法与集成方法（SPCI、ACI、AgACI），后者通过追踪分布偏移领先9-33%的相对Winkler分数。此外，它比训练后的神经预测器校准更优：在引入DeepNPTS的六个数据集上，朴素区间在名义95%置信水平下实际覆盖率为84-85%，而DeepNPTS仅为66%。在多步季节预测视界上，情况发生反转：随机游走基线成为最弱方法，季节性池（CSP）胜出——这一边界我们已绘制。最后，我们提出ConformalNaive+，一种单行、无训练、视界自适应的选择器，在每个视界上达到两个互补基线的较优者，且恢复覆盖。我们认为，每当学习型概率预测器声称性能提升时，匹配的共形朴素基线必须作为强制性基线。