Unsupervised marine vessel trajectory prediction using LSTM network and wild bootstrapping techniques - Venskus, Julius; Treigys, Povilas; Markevičiūtė, Jurgita (Nonlinear Analysis: Modelling and Control, 2021)
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 conditions. However, the task is burden by the fact that the vessels behave differently in different geographical sea regions, sea ports, and their trajectories depends on the vessel type as well. The model must learn spatio-temporal patterns representing vessel trajectories and should capture vessel’s position relation to both space and time. The authors of the paper proposes new unsupervised trajectory prediction with prediction regions at arbitrary probabilities using two methods: LSTM prediction region learning and wild bootstrapping. Results depict that both the autoencoder-based and wild bootstrapping region prediction algorithms can predict vessel trajectory and be applied for abnormal marine traffic detection by evaluating obtained prediction region in an unsupervised manner with desired prediction probability.
Keywords: unsupervised traffic anomaly prediction; LSTM prediction region learning; wild bootstrap; autoencoder; joint supervision