Unsupervised marine vessel trajectory prediction using LSTM network and wild bootstrapping techniques

Unsupervised marine vessel trajectory prediction using LSTM network and wild bootstrapping techniques

Increasing intensity in maritime traffic pushes the requirement in better preventionoriented incident management system. Observed regularities in data could help to predict vessel movement from previous vessels trajectory data and make further movement predictions under specific traffic and weather...

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Bibliographic Details
Main Authors: Julius Venskus, Povilas Treigys, Jurita Markevičiūtė
Format: Article
Language:English
Published: Vilnius University Press 2021-07-01
Series:Nonlinear Analysis
Subjects:
unsupervised traffic anomaly prediction
LSTM prediction region learning
wild bootstrap
autoencoder
joint supervision
Online Access:https://www.zurnalai.vu.lt/nonlinear-analysis/article/view/23056

Internet

https://www.zurnalai.vu.lt/nonlinear-analysis/article/view/23056

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