用时间感知验证方法，评估船舶油耗模型真实性能。
Time-Aware Validation of Machine Learning Fuel Consumption Models: Evidence from 1\,Hz Operational Data, CCGS \textit{Sir Wilfrid Laurier}
- 采用时间序列交叉验证，避免数据时间泄露
- 基于388万条1赫兹数据，验证模型真实预测能力
- 适合关注航运可持续性与模型部署可信度的研究者
船舶燃油消耗（SFC）预测支持船舶运营优化、排放估算及可持续航运决策支持系统。过去二十年开发了大量数据驱动的燃油模型，但其验证方法存在关键缺陷：多数研究使用随机训练-测试划分，应用于高频数据时会导致时间泄露，产生过于乐观的结果，无法反映实际部署条件。本文通过时间感知评估方法（时间序列交叉验证TSCV和阻塞式TSCV BTSCV），以加拿大海岸警卫队船‘西尔弗里德·劳里埃号’（CCGS Sir Wilfrid Laurier）为案例，对六种回归模型和一种物理基线模型，在三种时间感知方案和三种特征配置下进行调优，并在约388万条稳态1赫兹记录组成的时序预留集上进行统一评估。
原文摘要 · Abstract (English)
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.
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