Multi-Horizon Ship Speed Prediction with Temporal Attention Mechanism and GRU Encoder-Decoder

Abstract

Ship speed prediction is the basis for the realization of ship intelligence which can reduce energy consumption and protect the environment. We aim to propose a model for predicting accurate and timely ship speed. The speed prediction belongs to time series forecasting. In order to leverage the previous data among multi horizons and find highly non-linear relationships in long time range, we introduce a Gated Recurrent Unit (GRU) based encoder-decoder with temporal attention mechanism for speed prediction. The attention mechanism is adopted to assign different weights to ship speed of the input time steps. To validate the effectiveness of our model, we choose three baseline models to train and test on the same ship navigation dataset. The comparative experiment results suggest that our model has lower RMSE and MAE than the others. The proposed model in our paper performs better in ship speed prediction.