# VEPO-S2S: A VESsel Portrait Oriented Trajectory Prediction Model Based on S2S Framework
## 5 Experiments
### Model Performance Comparison
#### 5.2.3 Further Analysis  
In contrast to the evaluation of continuous trajectory prediction tasks, we conducted individual points evaluation using the RMSE, MAE, and FDE. As we can see from Tables 6-8, the prediction errors of all the models exhibited a noticeable increase from the first to the fifth prediction point. This is attributed to a significant reduction in the available information for each prediction, moving from the first to the last prediction. RNN baseline models employ a sliding window approach for prediction and gradually accumulate inaccuracies with each prediction. Conversely, the Seq2 Seq baseline models have the ability to simultaneously predict multiple points, which can reduce the tendency for error escalation compared with the RNN baseline models. Additionally, time series models rely on linear relationships between multiple time series; however, in long-term prediction tasks, nonlinear features become more prominent, causing inaccuracies to increase rapidly over time. Furthermore, our model almost outperforms all baselines. We also observe a similar performance between METO-S2S and VEPO-S2S at the fourth and fifth points in Table 6, indicating that both VEPO and METO are excellent models for long sequence prediction tasks. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model Name** & **First** & **Second** & **Third** & **Fourth** & **Fifth** \\ \hline Kalman & \(2.42\times 10^{-4}\) & \(3.27\times 10^{-4}\) & \(5.81\times 10^{-4}\) & \(8.15\times 10^{-4}\) & \(10.11\times 10^{-4}\) \\ VAR & \(0.98\times 10^{-4}\) & \(3.74\times 10^{-4}\) & \(86.61\times 10^{-4}\) & - & - \\ ARIMA & \(3.42\times 10^{-4}\) & \(6.63\times 10^{-4}\) & \(9.93\times 10^{-4}\) & \(13.28\times 10^{-4}\) & \(16.72\times 10^{-4}\) \\ LSTM & \(0.88\times 10^{-4}\) & \(2.10\times 10^{-4}\) & \(3.63\times 10^{-4}\) & \(5.43\times 10^{-4}\) & \(7.60\times 10^{-4}\) \\ BiLSTM & \(2.64\times 10^{-4}\) & \(2.65\times 10^{-4}\) & \(4.77\times 10^{-4}\) & \(6.39\times 10^{-4}\) & \(8.38\times 10^{-4}\) \\ GRU & \(0.86\times 10^{-4}\) & \(2.06\times 10^{-4}\) & \(3.52\times 10^{-4}\) & \(5.16\times 10^{-4}\) & \(6.96\times 10^{-4}\) \\ BiGRU & \(3.16\times 10^{-4}\) & \(2.42\times 10^{-4}\) & \(5.24\times 10^{-4}\) & \(6.44\times 10^{-4}\) & \(6.83\times 10^{-4}\) \\ LSTM-LSTM & \(1.22\times 10^{-4}\) & \(2.46\times 10^{-4}\) & \(3.90\times 10^{-4}\) & \(5.50\times 10^{-4}\) & \(7.20\times 10^{-4}\) \\ BiLSTM-LSTM & \(1.44\times 10^{-4}\) & \(2.77\times 10^{-4}\) & \(4.30\times 10^{-4}\) & \(5.95\times 10^{-4}\) & \(7.68\times 10^{-4}\) \\ GRU-GRU & \(0.98\times 10^{-4}\) & \(2.18\times 10^{-4}\) & \(3.59\times 10^{-4}\) & \(5.14\times 10^{-4}\) & \(6.84\times 10^{-4}\) \\ BiGRU-GRU & \(1.34\times 10^{-4}\) & \(2.82\times 10^{-4}\) & \(4.45\times 10^{-4}\) & \(6.17\times 10^{-4}\) & \(7.96\times 10^{-4}\) \\ Transformer & \(1.31\times 10^{-4}\) & \(2.08\times 10^{-4}\) & \(3.24\times 10^{-4}\) & \(4.59\times 10^{-4}\) & \(6.07\times 10^{-4}\) \\ METO-S2S & \(2.02\times 10^{-4}\) & \(2.03\times 10^{-4}\) & \(2.71\times 10^{-4}\) & \(\mathbf{3.76\times 10^{-4}}\) & \(5.23\times 10^{-4}\) \\ Ours & \(\mathbf{0.74\times 10^{-4}}\) & \(\mathbf{1.44\times 10^{-4}}\) & \(\mathbf{2.47\times 10^{-4}}\) & \(\mathbf{3.76\times 10^{-4}}\) & \(\mathbf{5.22\times 10^{-4}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The quantitative analysis results of each trajectory point under the RMSE evaluation index.