## 5 Conclusions  
This paper analyzes and summarizes the problems existing in current ship trajectory prediction methods. Considering the good global optimal fitting performance of SVR and its characteristics suitable for small sample training, an offline trajectory prediction method based on SVR was proposed. The speed, course, longitude, latitude and Unix time from AIS information were selected as the sample characteristic variables in the model to predict the time series of a ship's trajectory toward a better understanding of current and future trends of other ships and generating collision avoidance decisions with a certain foresight. However, the model proposed in this study is an offline model, which assumes that all samples are acquired at one time and cannot be modified once the model has been trained. AIS information is collected intermittently based on speed and heading. If newly acquired AIS data are significantly different from the original sample data, the prediction error is large. Therefore, establishing an incremental SVR-based trajectory prediction model is the next step that should be explored.  
Conceptualization, J.L. and K.Z.; methodology, J.L.; software, J.L. and K.Z.; validation, J.L., G.S. and K.Z.; formal analysis, J.L.; investigation, J.L.; resources, J.L.; data curation, J.L. and K.Z.; writing--original draft preparation, J.L.; writing--review and editing, J.L., G.S. and K.Z.; visualization, X.X.; supervision, G.S. and K.Z.  
This research was funded by the National Natural Science Foundation of China, grant number 51579025; the Natural Science Foundation of Liaoning Province, grant number 20170540090; and the Fundamental Research Funds for the Central Universities, grant number 3132018306.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Serial** & **Ship Name** & **Training** & **Test** & **\(\mathbf{E_{\text{MAX}}}\)/(\(\mathbf{{}^{\circ}}\))** & & \(\mathbf{MAE}\)/(\(\mathbf{{}^{\circ}}\))** & **Time/s** \\
**Letter** & **Stip/Group** & **Stip/Group** & **Logitude** & **Latitude** & **Longitude** & **Latitude** & **Latitude** \\ \hline a & ANASA & 143 & 26 & \(3.007\times 10^{-4}\) & \(2.119\times 10^{-4}\) & \(3.470\times 10^{-5}\) & \(5.048\times 10^{-5}\) & 46.5 \\ b & BULK INDIA & 159 & 26 & \(1.025\times 10^{-4}\) & \(4.090\times 10^{-5}\) & \(1.593\times 10^{-5}\) & \(6.592\times 10^{-6}\) & 50.7 \\ c & KAI ZHOU1 & 184 & 26 & \(2.288\times 10^{-5}\) & \(1.655\times 10^{-5}\) & \(3.736\times 10^{-6}\) & \(6.817\times 10^{-6}\) & 62.1 \\ d & CLYDE & 171 & 26 & \(4.317\times 10^{-5}\) & \(3.581\times 10^{-5}\) & \(1.846\times 10^{-5}\) & \(1.186\times 10^{-5}\) & 60.8 \\ e & DE PU & 182 & 26 & \(1.117\times 10^{-4}\) & \(5.676\times 10^{-5}\) & \(1.575\times 10^{-5}\) & \(4.347\times 10^{-5}\) & 64.4 \\ f & Star sirius & 123 & 26 & \(2.019\times 10^{-5}\) & \(3.953\times 10^{-5}\) & \(7.394\times 10^{-6}\) & \(1.522\times 10^{-5}\) & 41.3 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of prediction results for different ship trajectory sequences.  
Figure 13: Prediction results of f-trajectory using the ACDE-SVR model. (**a**) Comparison of predicted and actual values of f-trajectory; (**b**) Longitude and latitude absolute error values of f-trajectory.