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Life sciences · Preprint
arXiv · September 9, 2026
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TempTPI is a proposed deep learning architecture for maritime vessel trajectory prediction that combines an Informer encoder with temporal encoding. The work demonstrates improved computational efficiency and prediction accuracy on AIS data from Danish waters compared to one prior method, but lacks peer review, independent validation, and clinical or operational outcome data.
Comparative algorithm evaluation. AIS trajectory data from maritime vessels in Danish waters. Intervention: TempTPI: Informer-based encoder with multi-channel temporal encoding using Fourier-like frequency expansions. Compared with: TPTrans (state-of-the-art trajectory prediction architecture). Danish waters.
At 5-hour prediction horizon, TempTPI achieves 55% improvement in Mean Squared Error versus TPTrans baseline Model outperforms TPTrans across prediction windows of 1 to 5 hours Integrates ProbSparse self-attention to reduce computational complexity and focus on significant dependencies
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This is a computer science methods paper presenting a novel machine learning architecture for trajectory prediction, lacking clinical outcomes, human subjects, or validation against established clinical benchmarks relevant to healthcare professionals.
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Accurate long-term trajectory prediction for maritime vessels is essential for safety and logistical efficiency. While deep learning models, particularly Transformers, have shown promise in processing Automatic Identification System (AIS) data, they often struggle with the quadratic computational complexity of self-attention and the loss of accuracy over extended forecasting horizons. This study proposes TempTPI, a novel prediction framework that integrates an Informer-based encoder with a multi-channel temporal encoding mechanism. The Informer architecture leverages a ProbSparse self-attention mechanism to reduce computational overhead and focus on the most significant dependencies, while the temporal encoder utilizes Fourier-like frequency expansions to capture cyclic patterns (hourly, daily, and seasonal) in vessel behavior. We evaluate our model against the state-of-the-art TPTrans architecture using AIS data from Danish waters. Experimental results demonstrate that TempTPI consistently outperforms existing methods across prediction windows of 1 to 5 hours. Notably, at a 5-hour horizon, the proposed model achieves a 55% improvement in Mean Squared Error (MSE), offering a robust solution for long-range maritime situational awareness.
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