发表机构
National Research Council Canada; Memorial University of Newfoundland（加拿大国家研究委员会; 纽芬兰纪念大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对船舶燃油消耗模型验证的时间泄漏问题，采用时间序列交叉验证等方法，结合CCGS Sir Wilfrid Laurier号的1Hz运行数据，评估回归模型与物理基线的性能，为燃油消耗模型的可靠验证提供方案。
AI 中文摘要
船舶燃油消耗（SFC）预测可支持船舶运行优化、排放估算及可持续海事运输的决策支持系统（DSS）。过去二十年已开发出大量数据驱动的燃油模型，但存在一个关键且常被忽视的局限：多数研究采用随机训练-测试划分评估性能，该划分应用于高频记录时会引发时间泄漏，产生乐观结果，无法反映部署场景。本文针对该缺口开展研究，采用时间感知评估，具体为时间序列交叉验证（TSCV）和分块时间序列交叉验证（BTSCV）。以加拿大海岸警卫队船（CCGS）Sir Wilfrid Laurier号为案例，对六个回归模型和一个物理基线，在三种时间感知方案与三种特征配置下进行调参，随后基于从约388万条稳态1Hz记录中抽取的公共时间 hold-out 集进行评估。
英文摘要
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.
