Overview
Field: Machine Learning Authors: Samarasimha Reddy Chittamuru, Ayhan Akinturk, Allison Kennedy et al. (5 authors) Published: 2026-08-17 arXiv: 2608.16833
Summary
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) Sir Wilfrid Laurier as a case study, six regression models and a physics baseline were tuned under three time-aware validation schemes and three feature configurations, then evaluated on a common chronologically ordered hold-out set extracted from approximately 3.88 million steady-state 1 Hz records.
Key Contribution
The study demonstrates how time-aware validation protocols can reveal the extent to which conventional random-split evaluations overstate model performance, providing a more realistic benchmark for deploying ML fuel consumption models in maritime operations.
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