## 4 Results and Discussion
### Impact of Input Frequency Features on Model Performance  
This experiment was designed to assess the impact of features with different frequency resolutions on the overall performance in signal modeling and forecasting tasks, with the aim of validating the necessity and effectiveness of multi-frequency feature fusion in multi-scale sequence modeling. In complex system signal processing, low-frequency features typically reflect long-term trends and macro-level system behavior, while high-frequency features capture rapid short-term fluctuations and detailed changes. These two types of signals are inherently complementary. To empirically investigate this complementarity, three input configurations were constructed: low-frequency only, high-frequency only, and fused low- and high-frequency features. Each was fed into a unified model architecture to evaluate the classification and regression performance.  
As shown in Table 5, the model using low-frequency features alone achieved an F1-score of 87.9%, while the high-frequency-only configuration slightly improved the performance to 89.2%. The multi-frequency fusion model achieved the best results, with a precision of 92.4% and an F1-score of 92.0%, accompanied by significant reductions in error metrics such as MSE and MAE. These findings demonstrate that the structural hierarchy of feature frequency significantly influences modeling outcomes, and that multi-scale information fusion provides measurable benefits in handling non-stationary sequences. From a mathematical modeling perspective, this performance pattern can be attributed to the distinct representational roles of signals across frequency domains. Low-frequency features, being more stationary and trend-oriented, are critical for modeling long-term dependencies and establishing stable prediction baselines. However, reliance on low-frequency inputs alone limits the model's responsiveness to short-term disturbances or abrupt changes, often resulting in temporal lag in high-volatility scenarios. In contrast, high-frequency features offer superior temporal resolution and volatility sensitivity, enabling finer-grained pattern recognition and detection of transient anomalies. Nevertheless, their high variability also makes them more susceptible to noise, and in the absence of contextual constraints or trend anchoring, they tend to induce overfitting or localized prediction errors. The integration of low- and high-frequency features enables the model to balance global stability and local sensitivity, thus enhancing the expressive capacity of signal representation. Within the network architecture, this fusion is realized through mechanisms such as multi-scale temporal windows, shared weights, and attention-based cross-resolution alignment, allowing for joint perception and distributed modeling across frequency bands. As evidenced by the results, the fused model not only outperformed the other models in classification accuracy but also demonstrated lower prediction errors, indicating its superiority in capturing sequence distributions and modeling dynamic responses. These findings further support the critical role of multi-frequency fusion in complex sequence modeling tasks and provide structural justification for the development of temporally adaptive modeling systems.  
\begin{table}
\begin{tabular}{l c c c c c} \hline
**Market Condition** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** & **MSE** & **MAE** \\ \hline Stable Growth & 93.5 & 92.7 & 93.1 & 1.52 & 0.89 \\ High-Frequency Volatility & 91.6 & 90.8 & 91.2 & 1.83 & 0.99 \\ Overall Declining & 90.2 & 89.3 & 89.7 & 1.94 & 1.06 \\ \hline \end{tabular}
\end{table}
Table 4: Strategy performance comparison under different market conditions.