## 4 Results and Discussion
### Strategy Performance Comparison Under Different Data Environments  
This experiment was designed to evaluate the robustness and adaptability of the proposed multi-module signal modeling framework under varying data conditions. Specifically, three distinct scenarios were considered: a stable growth phase, a high-frequency volatility phase, and an overall declining phase. These settings were used to assess the model's predictive accuracy and error control capabilities across diverse temporal dynamics. The core objective of this experiment was to examine the model's ability to perceive non-stationary distributions, recover from localized disturbances, and accurately represent complex dynamic trajectories.  
As shown in Table 4, the model achieved the highest performance during the stable growth phase, with an F1-score of 93.1 and precision and recall reaching 93.5% and 92.7%, respectively. During the high-frequency volatility phase, the performance experienced a slight decline but remained above 91% across all classification metrics. In the overall declining phase, a further decrease in performance was observed, with the F1-score falling to 89.7 and a corresponding increase in error metrics. Nonetheless, the proposed framework consistently outperformed traditional architectures under all conditions. From the perspective of big data modeling and signal analysis, these results highlight the structural advantages of the multi-module collaborative framework in modeling nonlinear and non-stationary signals. In stable growth conditions characterized by strong trend dominance, signal structures exhibit high predictability. The multi-scale temporal modeling module effectively captures dominant frequencies and persistence, while the graph structure module can learn relatively stable inter-node dependencies, resulting in optimal performance. In the high-frequency volatility phase, where short-term perturbations and long-term trends are intertwined, the temporal module mitigates transient disturbances via multi-scale encoding. However, graph edge weights fluctuate more frequently, and the contribution of low-frequency macro factors is less pronounced in this context, leading to a slight but controlled performance drop. In contrast, during the overall declining phase, signals demonstrate more pronounced nonlinearity and abrupt transitions, such as sharp downturns and heavy-tailed noise. In such settings, the temporal module faces greater challenges due to elevated forecasting bias, and the volatility of graph adjacency structures diminishes the effectiveness of structural dependencies. Notably, the macro module, through the integration of semantic-level priors, provides critical resilience against such disruptions by compensating for local information loss and stabilizing predictions. Collectively, the findings from this experiment confirm the structural adaptability and noise resistance of the proposed heterogeneous multi-path framework when exposed to complex and variable input signals. The model demonstrates broad applicability and resilience in real-world deployments involving diverse data conditions.  
\begin{table}
\begin{tabular}{l c c c c c} \hline
**Configuration** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** & **MSE (\(\times 10^{-4}\))** & **MAE (\(\times 10^{-2}\))** \\ \hline w/o Temporal & 87.5 & 86.9 & 87.2 & 2.46 & 1.22 \\ w/o Macro & 88.1 & 87.3 & 87.7 & 2.42 & 1.19 \\ w/o Graph & 89.2 & 88.7 & 88.9 & 2.31 & 1.14 \\ Proposed & 92.4 & 91.6 & 92.0 & 1.76 & 0.96 \\ \hline \end{tabular}
\end{table}
Table 3: Performance comparison under module removal settings.