## 4 Experiment
### Experiment Results  
The experimental environment is depicted in Table 2. The comparisons on three datasets are depicted in Tables 3-5, respectively. The best results are bold and the second-best results are italic. We can make the following conclusions:  
* FC-LSTM performs worst among all the methods. This method models the spatial features via a fully connected layer, so it performs worse with increasing prediction length compared with other models. * As another typical RNN-based spatial-temporal prediction model, the performance of DCRNN is unsatisfactory compared with the others. The reason is that RNN is limited by its short and long-term dependencies extraction. * ASTGCN and GraphWaveNet perform better than STGCN overall because the former introduces attention mechanism and the latter adopts the adaptive adjacent matrix. This verifies that the attention mechanism and dynamic spatial dependencies modeling are effective. * STTNs is mainly composed of the self-attention mechanism and outperforms the ASTGCN, which fuses the attention mechanism with CNNs. This demonstrates that the self-attention mechanism captures the inner correlations in the series, improving the weights of important parameters that can better extract features of various scale. * Making the self-attention mechanism trend-aware and predicting the future data autoregressively, ASTGNN performs best in short-term prediction but is inferior in long-term prediction compared to the proposed MSSTTN and STTNs. It demonstrates that an autoregressive manner accumulates more errors in prediction. * Adopting learned graph and first-order gradients, FOGS performs relatively better on short-term than long-term predictions. * The MSSTTN model proposed in this study exhibits superior performance in long-term prediction across all datasets, achieving state-of-the-art results. Specifically, it is evident that the longer the future time steps, the better the prediction performances are. This verifies that MSSTTN is a powerful model for long-term spatial-temporal prediction. For the third step data prediction, MSSTTN fails to outperform all the baselines. This is because short-term patterns in spatial-temporal data are not complex, and the self-attention mechanism and CNNs can obtain their trend of change more accurately without excessive information exploitation. For the long-term patternsin spatial-temporal data, the GWNN improved by the multi-scale manner and auto-former improved by multi-scale series decomposition can enhance the spatial-temporal information utilization enormously compared to pure deep learning. MSSTTN has best performance in dealing with long-term spatial-temporal prediction. MSSTTN is improved based on STTNs, and the result that MSSTTN outperforms STTNs demonstrates that the improvements through multi-scale manners we make are practical. MSSTTN remains the dynamic spatial dependencies extraction module and then applies the scaling-learnable GWNN and autodformer with multi-scale series decomposition to improve the ability to capture fixed spatial dependencies and long-term temporal \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **MAE** & **MAPE (\%)** & **RMSE** \\ \hline STGCN & 22.20/23.68/26.27 & 16.08/17.55/19.94 & 34.45/36.34/39.76 \\ ASTGCN & 19.56/20.26/24.04 & 13.68/14.95/17.23 & 30.20/32.02/37.06 \\ GraphWaveNet & 18.93/20.16/22.10 & 13.15/14.05/15.59 & 29.92/31.68/33.46 \\ DCRNN & 20.50/21.87/24.62 & 16.60/18.04/21.05 & 33.44/35.12/38.47 \\ FC-LSTM & 36.51/42.74/56.8 & 50.36/57.67/72.45 & 39.62/42.64/75.24 \\ STTNs & 18.45/19.65/21.68 & 12.77/13.61/15.53 & **29.34**/31.09/33.73 \\ ASTGNN & **18.30**/19.74/22.08 & **12.75**/13.86/15.31 & 29.70/31.83/35.10 \\ FOGS & 18.45/19.66/21.84 & 13.10/13.99/15.84 & 29.61/31.42/34.34 \\ MSSTTN & 18.64/**18.97**/**20.05** & 13.16/**13.41**/**14.29** & 30.17/**30.81**/**32.39** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of PEMS04.