站定偏差：用于受监管动态系统时间序列填补的分层压力测试
The Stationarity Bias: Stratified Stress-Testing for Time-Series Imputation in Regulated Dynamical Systems
AI总结：
本文提出分层压力测试方法，揭示时间序列填补中站定偏差问题，通过CGM实验发现稳态效率、瞬态保真度及制度条件模型选择，强调深度学习在关键瞬态中的重要性。
摘要：
时间序列填补基准测试采用均匀随机遮蔽和不考虑形状的度量标准（MSE、RMSE），隐含地按制度 prevalence 加权评估。在具有主导吸引子——稳态生理、名义工业操作、稳定网络流量的系统中，这会产生系统性的站定偏差：简单方法似乎更优，因为基准测试主要采样易于低熵的制度，其中它们可以轻易成功。我们正式化了这种偏差，并提出了一种分层压力测试，将评估分为稳态和瞬态制度。使用连续葡萄糖监测（CGM）作为测试平台——因其严格的地面真实强迫函数（餐食、胰岛素）能够实现精确的制度识别——我们得出三个具有广泛影响的发现：（i）稳态效率：线性插值在稳定期间实现最先进的重建，证实了复杂架构在低熵制度中是计算浪费的。（ii）瞬态保真度：在关键瞬态（餐后峰值、低血糖事件）期间，线性方法表现出急剧下降的形态保真度（DTW），与RMSE不成比例——我们称之为RMSE幻象，其中低点误差掩盖了信号形状的破坏。（iii）制度条件模型选择：深度学习模型在瞬态期间保持点准确度和形态完整性，使其成为安全关键下游任务的必需品。我们进一步从临床试验中推导出经验缺失分布，并将其施加于完整训练数据上，防止模型利用不现实的清洁观测，鼓励在现实世界缺失情况下的鲁棒性。该框架可推广到任何受监管系统，其中常规稳态主导关键瞬态。
英文摘要：
Time-series imputation benchmarks employ uniform random masking and shape-agnostic metrics (MSE, RMSE), implicitly weighting evaluation by regime prevalence. In systems with a dominant attractor -- homeostatic physiology, nominal industrial operation, stable network traffic -- this creates a systematic \emph{Stationarity Bias}: simple methods appear superior because the benchmark predominantly samples the easy, low-entropy regime where they trivially succeed. We formalize this bias and propose a \emph{Stratified Stress-Test} that partitions evaluation into Stationary and Transient regimes. Using Continuous Glucose Monitoring (CGM) as a testbed -- chosen for its rigorous ground-truth forcing functions (meals, insulin) that enable precise regime identification -- we establish three findings with broad implications:(i)~Stationary Efficiency: Linear interpolation achieves state-of-the-art reconstruction during stable intervals, confirming that complex architectures are computationally wasteful in low-entropy regimes.(ii)~Transient Fidelity: During critical transients (post-prandial peaks, hypoglycemic events), linear methods exhibit drastically degraded morphological fidelity (DTW), disproportionate to their RMSE -- a phenomenon we term the \emph{RMSE Mirage}, where low pointwise error masks the destruction of signal shape.(iii)~Regime-Conditional Model Selection: Deep learning models preserve both pointwise accuracy and morphological integrity during transients, making them essential for safety-critical downstream tasks. We further derive empirical missingness distributions from clinical trials and impose them on complete training data, preventing models from exploiting unrealistically clean observations and encouraging robustness under real-world missingness. This framework generalizes to any regulated system where routine stationarity dominates critical transients.
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