## 5 Experiments
### Experiment Results
#### 5.4.3 Ablation Experiment  
To verify the role of each module of the model in prediction, we fixed the random seed and conducted a series of ablation experiments on the four datasets, which are the following: NO-PSA: remove the probability sparse self-attention; NO-MCTCN: remove the multichannel temporal convolution network; NP-PM: remove the prediction module; NO-RES: Remove residual connections.  
The experimental results are shown in Table 5 and Figure 7, and each module plays a role in the prediction. As shown in Figure 7d, the verification errors of different modules on the PEMS08 datasets, in the evaluation function of MAE, the errors of NO-PSA, NO-MCTCN, NO-PM, and NO-RES were increased by 3.91%, 20.84%, 16.09%, and 24.18% compared to the LGTCN, respectively. In the evaluation function of MAPE, the errors of NO-PSA, NO-MCTCN, NO-PM, and NO-RES were increased by 8.63%, 20.07%, 12.30%, and 23.52% compared to the LGTCN, respectively. In the evaluation function of the RMSE, the errors of NO-PSA, NO-MCTCN, NO-PM, and NO-RES were increased by 3.28%, 20.54%, 14.44%, and 25.02% compared to the LGTCN, respectively.  
After removing the PSA module, the model still showed strong predictive ability, because the multichannel mutual operation of the MCTCN mo  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline
**Datasets** & **Metrics** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** \\ \hline \multirow{3}{*}{PEMS03} & MAE & 13.31 & 13.92 & 14.44 & 14.86 & 15.23 & 15.53 & 15.79 & 16.02 & 16.30 & 16.65 & 17.06 & 17.63 \\  & MAPE(\%) & 13.38 & 13.78 & 14.12 & 14.41 & 14.71 & 15.00 & 15.28 & 15.50 & 15.77 & 16.12 & 16.54 & 17.10 \\  & RMSE & 20.32 & 21.53 & 22.53 & 23.32 & 23.98 & 24.49 & 24.91 & 25.30 & 25.74 & 26.30 & 26.93 & 27.79 \\ \hline \multirow{3}{*}{PEMS04} & MAE & 17.99 & 18.37 & 18.74 & 19.04 & 19.32 & 19.62 & 19.94 & 20.22 & 20.45 & 20.71 & 21.11 & 21.71 \\  & MAPE(\%) & 13.65 & 14.04 & 14.36 & 14.65 & 14.93 & 15.21 & 15.49 & 15.77 & 16.02 & 16.31 & 16.69 & 17.15 \\  & RMSE & 28.66 & 29.45 & 30.13 & 30.67 & 31.16 & 31.62 & 32.10 & 32.54 & 32.92 & 33.30 & 33.85 & 34.60 \\ \hline \multirow{3}{*}{PEMS07} & MAE & 18.47 & 19.81 & 20.77 & 21.49 & 22.07 & 22.64 & 23.23 & 23.83 & 24.46 & 25.10 & 25.84 & 26.83 \\  & MAPE(\%) & 8.39 & 9.00 & 9.44 & 9.81 & 10.13 & 10.43 & 10.74 & 11.05 & 11.33 & 11.66 & 12.07 & 12.61 \\  & RMSE & 28.58 & 30.94 & 32.47 & 33.60 & 34.52 & 35.36 & 36.20 & 37.01 & 37.83 & 38.67 & 39.63 & 40.84 \\ \hline \multirow{3}{*}{PEMS08} & MAE & 14.42 & 14.79 & 15.14 & 15.47 & 15.78 & 16.14 & 16.45 & 16.74 & 17.02 & 17.30 & 17.70 & 18.27 \\  & MAPE(\%) & 8.91 & 9.16 & 9.39 & 9.65 & 9.93 & 10.19 & 10.44 & 10.68 & 10.91 & 11.13 & 11.45 & 11.87 \\ \cline{1-1}  & RMSE & 21.97 & 22.77 & 23.49 & 24.16 & 24.76 & 25.35 & 25.88 & 26.37 & 26.80 & 27.21 & 27.73 & 28.49 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of errors in different prediction horizons on four datasets.  
Figure 6: Prediction errors on different datasets. (**a**) MAE at different horizon. (**b**) MAPE(%) at different horizon. (**c**) RMSE at different horizon.  
extraction of spatial relationships. The multichannel mechanism in the MCTCN module can achieve refined extraction for each node. Although the PM and RES modules are relatively simple, they have a very important effect on the stability of the model.