The prediction of ship trajectories is a growing field of study in artificial
intelligence. Traditional methods rely on the use of LSTM, GRU networks, and
even Transformer architectures for the prediction of spatio-temporal series.
This study proposes a viable alternative for predicting these trajectories
using only GNSS positions. It considers this spatio-temporal problem as a
natural language processing problem. The latitude/longitude coordinates of AIS
messages are transformed into cell identifiers using the H3 index. Thanks to
the pseudo-octal representation, it becomes easier for language models to learn
the spatial hierarchy of the H3 index. The method is compared with a classical
Kalman filter, widely used in the maritime domain, and introduces the Fr\'echet
distance as the main evaluation metric. We show that it is possible to predict
ship trajectories quite precisely up to 8 hours with 30 minutes of context. We
demonstrate that this alternative works well enough to predict trajectories
worldwide.

利用 GNSS 位置信息，本研究将船舶轨迹预测问题视为自然语言处理问题，通过 H3 指数将经纬度坐标转换为单元格标识符，并借助伪八进制表示使语言模型更容易学习 H3 指数的空间层次结构，该方法与传统的 Kalman 滤波器进行了比较，并引入了 Fréchet 距离作为主要评估指标。本研究表明，在 30 分钟的背景信息下，可以精确地预测船舶轨迹长达 8 小时，并且该方法在全球范围内都能够良好地预测轨迹。