  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.
