# Product Demand Prediction with Spatial Graph Neural Networks
## 5 Conclusions and Future Works  
### Conclusions  
In this paper, we introduced an innovative approach to product demand prediction through the utilization of the Spatial Graph Neural Network, tailored for the online retail domain. By adopting a novel methodology that intricately models the spatial relationships and attributes of retail locations, we have successfully demonstrated the potential of SGNN in transcending traditional demand prediction methods. Our approach, which constructs a spatially aware graph representation of the marketplace and integrates advanced attention mechanisms, aims to capture the nuanced relationships and dependencies that shape demand patterns.  
The comparative analysis against a comprehensive suite of baseline models, spanning linear, tree-based, and deep learning approaches, has underscored the SGNN framework's superior capability in accurately predicting demand. Through rigorous evaluation employing MAE, RMSE, and R\({}^{2}\), our findings reveal the SGNN model's adeptness at providing  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Batch Size**} & \multicolumn{3}{c}{**Avito**} \\ \cline{2-4}  & **MAE** & **RMSE** & **R\({}^{2}\)** \\ \hline
1000 & 0.1702 & 0.1535 & 0.9216 \\ \hline
2000 & 0.1685 & 0.1504 & 0.9234 \\ \hline
5000 & 0.1674 & 0.1492 & 0.9257 \\ \hline
10,000 & 0.1668 & 0.1487 & 0.9261 \\ \hline
20,000 & 0.1665 & 0.1483 & 0.9273 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The hyperparameter robustness analysis on the batch size.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Layer Number**} & \multicolumn{3}{c}{**Avito**} \\ \cline{2-4}  & **MAE** & **RMSE** & **R\({}^{2}\)** \\ \hline
1 & 0.1923 & 0.1794 & 0.9082 \\ \hline
2 & 0.1746 & 0.1593 & 0.9175 \\ \hline
3 & 0.1685 & 0.1504 & 0.9234 \\ \hline
4 & 0.1891 & 0.1750 & 0.9126 \\ \hline
5 & 0.2187 & 0.2023 & 0.8927 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The hyperparameter robustness analysis based on the layer number.  
actionable insights, thereby empowering retailers to navigate the intricacies of the online marketplace with enhanced strategic foresight.  
One of the key strengths of our SGNN approach lies in its ability to effectively capture and leverage spatial dependencies in demand patterns. By constructing a graph representation of the marketplace, our model can inherently account for geographical proximity and regional influences on product demand, a feature that traditional methods often struggle to incorporate effectively. This spatial awareness allows for more nuanced predictions that consider the complex interplay between location and demand.  
While our SGNN approach demonstrates significant advantages, it is important to acknowledge its limitations. The computational complexity of SGNN models can pose challenges for very large-scale applications, particularly in scenarios with extremely high numbers of retail locations or products. Additionally, the model's performance is dependent on the quality and completeness of the spatial data available, which may not always be consistent across different marketplaces or regions. Furthermore, while our model effectively captures spatial relationships, it may not fully account for temporal dynamics in demand patterns, which could be crucial in certain contexts. These limitations point to potential areas for future research and refinement of the SGNN approach.