## 2 Related Works
The advent of attention mechanisms signifies their transition from supplementary functions to fundamental modeling tools, establishing them as essential technologies in fields including image processing, speech recognition, knowledge graphs, and recommender systems. Specifically, within the crucial domain of traffic flow prediction, integrating deep learning techniques with attention mechanisms has led researchers to propose various innovative models to enhance prediction accuracy and reliability. For instance, [PERSON] et al. [26] introduced the innovative GGCN-SA, a deep learning-based traffic flow prediction model. It leverages a Graph Convolutional Network (GCN) for spatial correlation and Gated Recurrent Units (GRUs) for temporal dependence, further employing a soft attention mechanism to aggregate spatiotemporal information selectively, thereby improving its characterization of traffic flow's spatio-temporal features. [PERSON] et al. [27] proposed STAtt-Net, a novel Spatial and Temporal Attention-based Convolutional Network framework, to accurately predict citywide traffic flow. Initially, the system represents traffic data as a two-dimensional, two-channel matrix, and each cell correlates with the traffic flow of a specific area. Acknowledging the transportation system's temporal correlation and dependence, Researchers delineate the periodic patterns of the traffic data into three principal components: weekly trends, daily periodicity, and hourly proximity. Subsequently, STAtt-Net, utilizing the attention mechanism with STBlock as the foundational unit, learns the traffic flow's temporal and spatial dependencies. This approach achieves superior prediction performance, particularly in addressing issues like spatial non-stationarity that challenge traditional methods. Given the nonlinear and stochastic characteristics inherent to time-series data, including temperature changes, rainfall, interest rate fluctuations, exchange rate fluctuations, and traffic flow, accurate analysis and forecasting of these data are paramount. Such characteristics indicate that simple linear models are insufficient for capturing the complex interrelations among data, necessitating advanced nonlinear models like neural networks and support vector machines to uncover underlying connections. Randomness implies that data values or their occurrence probabilities are indeterminate, adhering to certain probability distributions or stochastic processes, necessitating the application of mathematical tools like probability theory, mathematical statistics, and stochastic methods for their analysis and processing. Additionally, data instability, reflected in the variability of statistical properties like mean, variance, and covariance over time or space, necessitates employing techniques such as smoothing, differencing, and covariance analysis for effective management. Considering the earlier analysis, accurately analyzing and predicting time-series data--including temperature changes, rainfall, interest rate fluctuations, exchange rate fluctuations, and traffic flow--presents a formidable research challenge with significant practical application value. Consequently, numerous scholars have investigated the pre-model training decomposition of data to improve its smoothness, thereby augmenting the model's capability to discern the data's intrinsic structure. These decomposition techniques transform complex, nonlinear, and nonsmooth raw data into stable subsequences that are more amenable to analysis and prediction, thereby revealing more profound data properties and laying a solid foundation for subsequent model training and prediction. In this endeavor, scholars persist in refining and innovating decomposition techniques to suit various data types, further enhancing the accuracy and efficiency of time-series analysis and prediction. [PERSON] et al. [28] initially employed wavelet decomposition to categorize original traffic flow data into high-frequency and low-frequency subsets, enhancing data stability.