AI 中文总结
TEMPER是一种结合时间自动编码器、可微掩蔽神经决策森林和CRPS训练的单变量时间序列概率预测算法，在合成序列上表现出良好的校准和长水平预测性能。
AI 中文摘要
概率预测需要准确的中心预测和校准的不确定性估计。本文提出TEMPER，即时间编码器掩蔽概率集成回归器，一种单变量时间序列预测算法，它结合了时间自动编码器、可微掩蔽神经决策森林、连续排序概率得分（CRPS）训练以及高斯混合后处理。其R实现基于torch for R构建，并返回逐水平密度、分布、分位数和采样器函数。我们在三个具有趋势、周期性、机制切换、非线性阈值和异方差成分的确定性合成水平序列上评估TEMPER。在t+1、t+5、t+20和t+60水平上的96次滚动原点预测中，TEMPER在300轮上限和早停耐心为100的训练后，获得了按原点水平归一化的平均CRPS为2.824%，中位绝对误差为3.635%，经验90%区间覆盖率为68.8%。朴素持久性自举法具有最佳总体CRPS，为2.763%，而TEMPER在中位绝对误差以及t+1和t+5的CRPS上表现最佳。消融研究使用匹配的序列-原点-水平单元、逐水平CRPS差值、端点敏感性总结以及特定于校准的区间研究。放宽学习掩码在消融子集上将平均CRPS提高了0.472个百分点，主要来自长水平增益。两倍区间膨胀将留出覆盖率从54.2%提高到91.7%，并在测试的校准规则中给出了最佳的90%区间得分。结果确定了校准、水平特定调优和成分选择为核心研究重点。
英文摘要
Probabilistic forecasting requires accurate central predictions and calibrated uncertainty estimates. This paper presents TEMPER, the Temporal Encoder-Masked Probabilistic Ensemble Regressor, a univariate time-series forecasting algorithm that combines a temporal autoencoder, a differentiable masked neural decision forest, continuous ranked probability score (CRPS) training, and Gaussian-mixture post-processing. The R implementation is built on torch for R and returns horizon-wise density, distribution, quantile, and sampler functions. We evaluate TEMPER on three deterministic synthetic level series with trend, periodic, regime-switching, nonlinear-threshold, and heteroskedastic components. Across 96 rolling-origin forecasts at horizons t + 1, t + 5, t + 20, and t + 60, TEMPER obtains 2.824% mean CRPS normalized by origin level, 3.635% median absolute error, and 68.8% empirical 90% interval coverage after training with a 300-epoch cap and early-stopping patience of 100. A naive persistence bootstrap has the best aggregate CRPS, 2.763%, while TEMPER has the best median absolute error and the best CRPS at t+1 and t+5. The ablation study uses matched series-origin-horizon cells, horizon-wise CRPS deltas, endpoint sensitivity summaries, and a calibration-specific interval study. Relaxing the learned mask improves average CRPS by 0.472 percentage points on the ablation subset, mainly through long-horizon gains. A twofold interval inflation improves held-out coverage from 54.2% to 91.7% and gives the best 90% interval score among tested calibration rules. The results identify calibration, horizon-specific tuning, and component selection as the central research priorities.
