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.

本文针对海洋数据中存在时间奇异性的问题，使用神经网络建模，预测多艘船舶的 AIS 消息内容，以改善航线预测。在多个算法实验中，卷积层、前馈网络和递归神经网络的组合模型表现最佳。结果表明该模型有效地提高了航线预测的准确度。