arXiv (All)
open access
Protocol before progress: leakage-aware evaluation of AIS trajectory prediction
Reported gains in vessel-trajectory prediction from Automatic Identification System (AIS) data are credited to new architectures, but the evaluation protocol is rarely measured as a source of error reduction. We build a leakage-aware protocol with vessel-, time- and region-disjoint splits and apply
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- TempTPI: Informer-Based trajectory prediction for maritime vessels#996658
- A Large-Scale AIS Dataset from Finnish Water#1070386
- An extension of the linear regression model for improved vessel trajectory prediction utilising a priori AIS Information#469997
- Predicting Delayed Train Trajectories on the Dutch Railway Network: Explainable AI Evaluation of Topological, Operational and Weather Features with Tree Based Ensemble Methods#1128951
- Evaluating Graph Neural Networks for Change-Criticality Classification in Maritime Navigation Charts#925369
Record · ID 1061455
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive