## 4 Results and Discussion
### Performance Comparison Under Module Removal Settings  
This experiment was designed to analyze the independent contributions of each submodule within the proposed multi-module deep architecture, specifically evaluating the roles and relative importance of temporal modeling, graph-based structural modeling, and macro-level perturbation modeling. By sequentially removing each of the three core modules and comparing the resulting performance changes in both classification and regression tasks with respect to the complete model, the criticality of each module in high-dimensional signal processing could be quantitatively assessed. This experiment constituted a classical structural ablation test, whose underlying objective was to examine the marginal contribution of distinct information dimensions--such as temporal dependency, spatial structure, and external semantics--to the overall predictive performance within a complex modeling framework. As shown in Table 3, the complete model achieved the highest scores across all evaluation metrics, with a precision of 92.4%, a recall of 91.6%, and an F1-score of 92.0%. The removal of any of the three modules led to a measurable performance degradation, with the most significant decline observed when the temporal modeling module was excluded, where the F1-score dropped to 87.2. This indicates a substantial reduction in the model's ability to capture dynamic signal evolution. From a theoretical modeling perspective, the three sub-modules undertake complementary signal processing tasks with distinct functional emphases. The graph module introduces non-Euclidean adjacency structures by constructing relational graphs among entities, effectively capturing interdependencies between input signals, such as co-movement patterns across assets, features, or observational units in historical data. By aggregating node features within local neighborhoods, this module maintains structural consistency, enabling stable representation of hierarchical changes, even in high-dimensional settings. The macro module, on the other hand, encodes low-frequency external perturbations by transforming policy changes and macroeconomic shifts into sentiment vectors that are fused with internal signal dynamics. Although its direct impact on performance is less pronounced than the graph module, it substantially enhances the model's robustness during disruptive events or periods of elevated uncertainty. The temporal module serves as the backbone of the overall sequence modeling pipeline. It is responsible for capturing dynamic temporal dependencies and multi-scale evolution patterns. Leveraging a Transformer-based architecture, this module offers superior global context perception and high-dimensional parallel modeling compared to traditional RNN or convolutional approaches. The most significant performance drop observed when this module was removed underscores its critical role in representing the temporal characteristics of signals, which are fundamental to the model's predictive capabilities. From a mathematical modeling standpoint, the graph module introduces spatial correlation learning, the macro module provides conditional perturbation modeling, and the temporal module governs the encoding of global dynamic trajectories. Together, \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** & **MSE (\(\times 10^{-4}\))** & **MAE (\(\times 10^{-2}\))** \\ \hline SVM & 81.4 & 80.9 & 81.1 & 3.12 & 1.42 \\ XGBoost & 82.7 & 81.9 & 82.3 & 2.78 & 1.33 \\ LightGBM & 83.5 & 82.6 & 83.0 & 2.65 & 1.28 \\ LSTM & 85.8 & 85.1 & 85.4 & 2.39 & 1.19 \\ TCN & 86.9 & 86.4 & 86.6 & 2.21 & 1.12 \\ FinBERT & 87.1 & 86.5 & 86.8 & 2.18 & 1.10 \\ StockGCN & 89.3 & 88.0 & 88.6 & 2.04 & 1.07 \\ Proposed Method & 92.4 & 91.6 & 92.0 & 1.76 & 0.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification and regression results on signal sequences.