## 4 Results and Discussion
### Classification and Regression Results on Signal Sequences  
This experiment was designed to evaluate the performance differences across various types of models in handling high-frequency signal sequence classification and continuous-value prediction tasks, aiming to assess the proposed framework in terms of accuracy, stability, and error control. The task is inherently a multivariate nonlinear time series analysis problem, characterized by structural complexity, frequent fluctuations, and multi-scale interactions. Therefore, an effective model is expected to balance local feature extraction, long-range dependency modeling, and multi-source information integration. Comparative experiments were conducted using traditional machine learning models (SVM, XGBoost, LightGBM), deep sequence models (LSTM, TCN), pre-trained semantic models (FinBERT), structure-aware graph models (StockGCN), and the proposed multimodal modeling framework.  
As shown in Table 2, in terms of classification metrics, the traditional models generally achieved precisions between 81% and 83%. With enhanced sensitivity to temporal sequences, models such as LSTM and TCN achieved precisions exceeding 85%. FinBERT, equipped with capabilities for processing unstructured semantic information, exhibited a notable advantage in signal discrimination. StockGCN, by modeling graph-based structural relationships, demonstrated improved stability in handling high-frequency feature variations. The proposed method outperformed all baseline models across all metrics, achieving a precision of 92.4% and an F1-score of 92.0%, indicating a systematic improvement in overall signal modeling capabilities. From the perspective of model architecture, traditional models such as SVM, relying on static hyperplane separation, are limited in capturing dynamic temporal patterns, resulting in suboptimal recall and F1 performance. While tree-based models like XGBoost and LightGBM can fit complex feature relationships, they exhibit structural limitations in modeling temporal dependencies, leading to challenges in responding to high-frequency volatility. In contrast, LSTM introduces long-term memory capabilities through gated mechanisms, significantly enhancing sequential modeling. TCN further replaces recurrent operations with 1D convolutions, improving parallelism and capturing long-range dependencies, which proves beneficial for continuous signal prediction. FinBERT, utilizing multi-layer Transformer structures, offers inherent advantages in processing external textual signals. StockGCN captures inter-signal structural correlations through graph convolution operations over topological asset relations, making it suitable for modeling high-frequency interactions among multiple entities. The proposed method builds upon these strengths by integrating a graph attention mechanism, multi-scale Transformer encoding, and macro-semantic embeddings generated by LLMs. Through three-path representation learning and a unified fusion layer, the framework enables joint modeling across structural, temporal, and external semantic dimensions. This holistic design enhances the model's capability in perceiving nonlinear signal behavior, multi-dimensional dependencies, and heterogeneous information. As a result, the framework demonstrated superior adaptability and robustness, supporting more accurate and stable predictions under complex signal environments.