###### Abstract
combined Transformer with GCN, utilizing attention mechanisms to learn multiple sensors' temporal and spatial dependencies separately, thereby improving the accuracy and efficiency of predictions at different time granularities [13]. To further enhance the accuracy of spatiotemporal predictions, many scholars propose utilizing adjacency matrices containing richer information to assist the model. For example, [PERSON] et al. introduced the Dynamic Time Warping (DTW) method to generate similarity matrices to help extract information, thereby improving the model's predictive performance [14]. [PERSON] et al. summarized the performance enhancement of GCN-based spatiotemporal prediction models based on predefined adjacency matrices calculated with different similarities [15]. However, these methods do not jump over the limitations of existing spatiotemporal relations. In statistics, the Granger causality test method is widely applied to analyze the relationship between two-time series. For instance, [PERSON] et al. utilized the Granger causality test method to investigate the correlation between different sectors of Chinese and American stocks, thereby forming a causal relationship network [16]. This facilitates a transition in the analysis of sensor network relationships from a spatial perspective to a causal domain. Although these methods extend the approach to modeling spatiotemporal sequences, almost all methods use historical and predictive windows for model training, so enhancing the density of information encapsulated within an individual window has been identified as a potent strategy to augment the model's predictive capabilities. Some scholars proposed decomposing series at the frequency level, employing techniques like Empirical Mode Decomposition (EMD). Combining Empirical Mode Decomposition (EMD) with deep learning models enables the extraction of time patterns across different frequency components in time series data, which may be challenging for models to learn directly from raw data. This method has been applied in various fields. For example, [PERSON] et al. combined EMD with LSTM to refine the accuracy of long-term deformation prediction for tailings dams, achieving this by aggregating the results from various LSTM model components [17]. [PERSON] et al. designed an EMD-Particle Swarm Optimization-Gaussian process regression combination model, achieving accurate stress forecasting in ultra-high arch dams [18]. The application of EEMD methods in water demand forecasting has been relatively limited. Overall, despite the good predictive results achieved by existing methods, the following challenges still need to be overcome:  
1. Existing models that analyze spatiotemporal data often treat spatial and temporal relationships as separate entities. This separation can lead to significant information loss, subsequently diminishing the accuracy of the model's predictions. 2. Furthermore, when dealing with natural scenes, the spatial relationships between sensors might not always be apparent or accessible, complicating the model's learning process. Consequently, there is a pressing need for innovative graph-building techniques that can facilitate adequate information flow among sensor nodes without relying on predefined spatial relationships. 3. Additionally, assigning a distinct time series model to each series decomposed using the Empirical Mode Decomposition (EMD) method has proven inefficient. A more streamlined approach is required to fully capitalize on the insights from the decomposed series fully, enhancing the overall model efficiency. Addressing these challenges, we introduce the Ensemble EMD (EEMD) Granger causality test Dynamic Graph Attention Transformer Network (EG-DGATN) model, intending to improve the accuracy of water demand forecasting tasks for multiple regional geographic units without relying on prior graph structures. Different from other methods, this model imposes no specific constraints on data input and offers the following contributions:  
1.