# STFEformer: Spatial-Temporal Fusion Embedding Transformer for Traffic Flow Prediction
## 3 Experiments
### Baselines  
We compared STFEformer with 12 baseline models. Descriptions of these baseline methods are provided as follows:  
VAR [39]: Vector Auto-Regression (VAR) is a statistical model utilized for time-series data analysis. It captures the dynamic relationships between variables. In this model, each variable is represented as a linear function influenced by its own historical values as well as those of other variables, thus facilitating the simultaneous analysis of several time series. SVR [40]: Support Vector Regression (SVR) is a regression method derived from the principles of the Support Vector Machine (SVM). The core idea of SVR is to determine a decision boundary, namely, a regression hyperplane, that maximizes the margin to the nearest training sample points. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Dataset** & **No. of Nodes** & **No. of Edges** & **No. of Time Steps** & **Time Interval** & **Time Range** \\ \hline PeMS04 & 307 & 340 & 16,992 & 5 min & 1 January–28 February 2018 \\ PeMS07 & 883 & 866 & 28,224 & 5 min & 1 May–31 August 2017 \\ PeMS08 & 170 & 295 & 17,856 & 5 min & 1 July–31 August 2016 \\ \hline \end{tabular}
\end{table} 
Table 3: Dataset description. DCRNN [7]: The Diffusion Convolutional Recurrent Neural Network (DCRNN) models traffic flow as a diffusion process in directed graphs. It introduces bidirectional random walks in the graphs to explore spatial features and utilizes an encoder-decoder architecture with Gated Recurrent Units (GRUs) to learn temporal correlations. ASTGCN [41]: The Attention-Based Spatial-Temporal Graph Convolutional Network (ASTGCN) uses approximate expansion of Chebyshev polynomials to explore spatial dependencies and a standard convolution layer to model temporal features. Additionally, a spatial-temporal attention mechanism is employed to capture the dynamic spatial-temporal correlations in traffic data. STSGCN [23]: The Spatial-Temporal Synchronous Graph Convolutional Network (STSGCN) introduces a novel spatial-temporal graph convolutional module that synchronizes the capture of local spatial-temporal dependencies. Additionally, a multiple-module layer was designed to explore heterogeneity in spatial-temporal graphs. It deploys multiple modules in each time period, allowing each module to concentrate on extracting the spatial-temporal dependencies from localized spatial-temporal graphs. AGCRN [9]: The Adaptive Graph Convolutional Recurrent Network (AGCRN) designs a node adaptive parameter learning module to explore spatial features for each node. It also introduces a data adaptive graph generation module to automatically capture the intercorrelations between various tensors. Moreover, the two modules are combined with Gated Recurrent Units (GRUs) to learn temporal dependencies. GMAN [25]: The Graph Multi-Attention Network (GMAN) proposes a spatial-temporal attention mechanism to explore dynamic spatial and non-linear temporal correlations. To adaptively fuse the outputs derived from spatial-temporal attention, gated fusion was also designed. Furthermore, it employs a transform attention mechanism to obtain prediction representations from historical features. Z-GCNETs [42]: Time-Aware Zigzags at Graph Convolutional Networks (Z-GCNETs) is a pioneering approach that merges time-conditioned DL with time-aware persistent homology data representations. A zigzag topological layer designed specifically for time-aware graph convolutional networks was developed and integrated with Gated Recurrent Units (GRUs). This model is able not only to capture the topological attributes of data but also to comprehend how these characteristics evolve over time. STFGNN [22]: The Spatial-Temporal Fusion Graph Neural Network (STFGNN) designs a novel adjacency matrix via row data and is capable of extracting features that spatial graphs may not reflect. Additionally, by integrating a dilated CNN module with a gated mechanism and a spatial-temporal fusion graph module, it can explore long-range and long-term spatial-temporal correlations through layer stacking.