# Predicting Vessel Trajectories Using ASTGCN with StemGNN-Derived Correlation Matrix
## 5 Experiment and Analysis
### Experimental Results Analysis  
We deeply discussed the prediction accuracy of the five models on train/val/test track data and the results shown in Figures 3-5. According to the training and testing results of each model, CNN_LSTM_CBAM has relatively weak performance, high error, and the slowest convergence speed. The other four models are similar in performance, but ASTGCN has the smallest error and the fastest convergence on the training data set. StemGNN and STGCN followed closely behind, while TCN performed slightly worse than STGCN. ASTGCN showed the best performance on the validation dataset (val) and test dataset (test). The error is quite close to the error on the training data set, and the results are stable. StemGNN and STGCN followed closely behind, while TCN showed clear signs of volatility and elevated errors. CNN_LSTM_CBAM continues to underperform on all datasets. In conclusion, ASTGCN shows excellent performance and stability in this experiment. Compared with other models, it shows lower error and faster convergence rates on training and validation data sets. This result shows that ASTGCN has strong potential and capability in processing spatiotemporal trajectory data. Figure 3: Error situation of each model on the training dataset: (**a**) Comparison chart of L1 indicators; (**b**) Comparison chart of L1 indicator details; (**c**) Comparison chart of MSE indicators; (**d**) Comparison chart of MSE indicator details. Figure 4: Error situation of each model on the val dataset: (**a**) Comparison chart of L1 indicators; (**b**) Comparison chart of L1 indicator details; (**c**) Comparison chart of MSE indicators; (**d**) Comparison chart of MSE indicator details. Figure 5: Error situation of each model on the test dataset: (**a**) Comparison chart of L1 indicators; (**b**) Comparison chart of L1 indicator details; (**c**) Comparison chart of MSE indicators; (**d**) Comparison chart of MSE indicator details. By calculating the accuracy of each trajectory prediction model, the performance of each model in predicting trajectory data is deeply discussed. The statistical results are shown in Table 3. It can be seen from the data in the table that the prediction accuracy of the ASTGCN model is superior to other models on each data set. Among them, it can be seen from the prediction of CNN_LSTM_CBAM and TCN that although the relevant models introduce standard convolution technology, which is helpful for trajectory prediction, they only consider the local information of each position of the input data, ignoring the important information of the time dimension. This makes these models limited in making full use of the timing feature information in vessel trajectories. In contrast, graph-based trajectory models such as StemGNN, STGCN, and ASTGCN are more suitable for dealing with vessel trajectories with complex nonlinear relationships. These models can capture the dependencies between features and the pattern information in time series to provide more reliable and stable vessel trajectory prediction results. In summary, the accuracy of vessel trajectory prediction of the ASTGCN model is significantly better than that of other models on each data set, especially on training and verification data sets, and the error is stable and kept at a low level. This shows that the ASTGCN model has high trajectory prediction accuracy and good generalization ability and can effectively adapt to various vessel trajectory data. By calculating the accuracy of each trajectory prediction model, the performance of each model in predicting trajectory data is presented. The statistical results are shown in Table 3. It can be seen from the data in the table that the prediction accuracy of the ASTGCN model is superior to other models on each data set.