这篇论文用388万条1Hz实船数据证明，随机划分评估会让油耗模型成绩虚高，时间感知验证才靠谱。
多数船舶油耗模型验证采用随机划分训练/测试集，在1Hz高频数据上会产生时间泄漏，使性能虚高。这项研究以加拿大海岸警卫队舰船CCGS Sir Wilfrid Laurier为案例，测试6个回归模型和1个物理基线模型。所有模型在3种时间感知验证方案和3种特征配置下完成调优，评估集为约388万条稳态1Hz记录。论文对比时间序列交叉验证（TSCV）与阻塞TSCV（BTSCV），考察两种方案能否反映真实部署条件。
Time-Aware Validation of Machine Learning Fuel Consumption Models: Evidence from 1\,Hz Operational Data, CCGS \textit{Sir Wilfrid Laurier}
Ship fuel consumption (SFC) prediction supports vessel operation optimisation, emissions estimation, and decision support systems (DSS) for sustainable maritime transportation. Numerous data-driven fuel models have been developed over the past two decades, but a critical and often overlooked limitation lies in their validation practices: most studies evaluate performance using random train--test splits, which, applied to high-frequency records, admit temporal leakage and yield optimistic results that do not reflect deployment conditions. This paper examines that gap using time-aware evaluation, specifically Time Series Cross-Validation (TSCV) and Blocked TSCV (BTSCV). Using the Canadian Coast Guard Ship (CCGS) \textit{Sir Wilfrid Laurier} as a case study, six regression models and a physics baseline are tuned under three time-aware schemes and three feature configurations, then evaluated on a common chronological hold-out set drawn from approximately 3.88 million steady-state 1\,Hz records.