## 4 Experiments  
To validate the performance of DG-STFN, several baselines are set, including traditional statistical models and cutting-edge deep learning models. * SARIMAX: A statistic-based method for time series, which introduces a seasonal term based on the ARIMA model polynomial. * LSTM: A special recurrent neural network (RNN) model for solving the long-term dependency. * STGCN [9]: A spatio-temporal graph convolutional network, which uses a convolutional structure to extract the spatio-temporal correlation of time series. * DCRNN [10]: A diffusion convolution recurrent neural network that uses diffusion convolution to capture spatial correlation combined with a Seq2 Seq architecture to capture temporal correlation. * ASTGCN [12]: An attention-based spatio-temporal graph convolutional network with an attention mechanism for traffic flow prediction is designed to analyze the spatio-temporal features of time series. In this paper, all experiments are executed on an NVIDIA GeoForce GTX 1050 GPU with 4 GB memory. All of the models are training in Python 3.8.13 with Pytorch 1.12.1. Experiments are conducted with all models on the same dataset, which is split into a training, validation, and test set in an 8:1:1 ratio. As for the parameter settings of the models, the common parameter settings are shown in Table 3, and the other parameter settings are consistent with their original papers. For other parameter settings of the DG-STFN, we utilize a third-order Chebyshev polynomial as the graph convolution kernel, set the signal input and output channel numbers of the 1D-CNN to 64, and specify the convolution kernel size as (1, 3) with a stride of 1. For the prediction task, the time series of 24 historical time steps are used to predict the data of the next 3, 6, 9, and 12 time steps, respectively. The results are obtained by averaging several rounds of training. The detailed results are shown in Table 4.  
As shown in Table 4, SARIMAX performs the worst on both datasets. This model is suitable for dealing with stationarity and linear time series, but the time series in this experiment has complex nonlinear relationships, so the performance of SARIMA is the worst. In the deep learning model, the method based on GCN o \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**ModelParameter**} & \multicolumn{4}{c}{**WSD**} & \multicolumn{4}{c}{**APD**} \\ \cline{2-9}  & **Epoch** & **Batch Size** & **Optimizer** & **Learning Rate** & **Epoch** & **Batch Size** & **Optimizer** & **Learning Rate** \\ \hline LSTM & 100 & 8 & [PERSON] & 0.001 & 100 & 16 & [PERSON] & 0.005 \\ STGCN & 100 & 8 & [PERSON] & 0.005 & 100 & 16 & [PERSON] & 0.005 \\ DCRNN & 100 & 8 & [PERSON] & 0.005 & 100 & 16 & [PERSON] & 0.001 \\ ASTGCN & 100 & 8 & [PERSON] & 0.005 & 100 & 16 & [PERSON] & 0.005 \\ DG-STFN & 100 & 8 & [PERSON] & 0.002 & 100 & 16 & [PERSON] & 0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The common parameter settings of each model. LSTM, which shows to some extent that to solve the prediction problem, we should not only consider the time dependence in the time dimension but also give full consideration to the influence of spatial correlation between series. Generally speaking, the method based on GCN performs better because of its ability to extract spatial and temporal features. In GCN-based methods, both DCRNN and STGCN rely on a static predefined graph structure, and DCRNN performs better than STGCN on both datasets. This may be due to the fact that STGCN is a single-step prediction model, and the way it achieves multi-step prediction by overlaying single-step prediction results causes the problem of error accumulation, which in turn leads to poor performance. ASTGCN outperforms DCRNN and STGCN because it designs an attention mechanism that can correct the predefined graph structure. Compared with the baselines, DG-STFN fully analyses the correlation of the time series in the spatial dimension and the long-term dependence in the temporal dimension.