  The oceans are a source of an impressive mixture of complex data that could
be used to uncover relationships yet to be discovered. Such data comes from the
oceans and their surface, such as Automatic Identification System (AIS)
messages used for tracking vessels' trajectories. AIS messages are transmitted
over radio or satellite at ideally periodic time intervals but vary irregularly
over time. As such, this paper aims to model the AIS message transmission
behavior through neural networks for forecasting upcoming AIS messages' content
from multiple vessels, particularly in a simultaneous approach despite
messages' temporal irregularities as outliers. We present a set of experiments
comprising multiple algorithms for forecasting tasks with horizon sizes of
varying lengths. Deep learning models (e.g., neural networks) revealed
themselves to adequately preserve vessels' spatial awareness regardless of
temporal irregularity. We show how convolutional layers, feed-forward networks,
and recurrent neural networks can improve such tasks by working together.
Experimenting with short, medium, and large-sized sequences of messages, our
model achieved 36/37/38% of the Relative Percentage Difference - the lower, the
better, whereas we observed 92/45/96% on the Elman's RNN, 51/52/40% on the GRU,
and 129/98/61% on the LSTM. These results support our model as a driver for
improving the prediction of vessel routes when analyzing multiple vessels of
diverging types simultaneously under temporally noise data.
