# Hyperband-Optimized CNN-BiLSTM with Attention Mechanism for Corporate Financial Distress Prediction
## 5 Results
### Analysis of the Prediction Effect of Hyperband-CNN-BiLSTM-AT Model
#### 5.1.3 Comparison of Results from Different Models  
Figures 9 and 10, along with Table 12, show the accuracy comparison results of six different models (w = 8, batch_size = 32), namely CNN, BiLSTM, CNN-BiLSTM, CNN-AT, BiLSTM-AT, CNN-GRU, Transformer, and CNN-BiLSTM-AT. From the figure, it can be seen that the CNN-BiLSTM-AT model performed the best with an accuracy of 0.994. (1) CNN model. The accuracy of this model was 0.974, showing it performed relatively well. However, it had the lowest accuracy among all models, mainly when predicting ST companies. Among the 118 actual ST sequence data points, 22 were mispredicted, and the recall rate was only 0.814. This indicates that using the CNN alone may not achieve optimal results when dealing with specific tasks. In addition, the model had a training time of 162.52 s, the shortest training time, and therefore is mainly suitable for use in scenarios with limited computing resources or requiring fast training. Figure 8: Distribution of feature importance ranking for the best CNN-BiLSTM-AT model. Figure 9: Comparison of accuracy results of different models. BiLSTM model. The accuracy of this model was 0.985, which is significantly improved compared with the simple CNN model. The recall rate of ST samples was increased to 0.932, indicating that the model performs stably in identifying ST samples and performs better for time series data. In addition, the model took 1279.94 s to train, the longest training time. Although it performed well, the training time was relatively long, making it suitable for use in scenarios where resources are abundant or time is not tight. (3) CNN-BiLSTM model. This model's accuracy was 0.990, performing better than CNN or BiLSTM. It also improved the accuracy and recall of predicting ST companies. In addition, the model's training time was 798.16 s, which was shorter than the training time of the BiLSTM model alone. This indicates that the CNN can effectively extract features in the model, reduce the computational burden of BiLSTM, and thus accelerate the training speed. (4) CNN-AT model. The accuracy of this model was 0.991, which was better than using the CNN alone. The accuracy and recall of predicting ST companies were significantly improved, indicating that the combination of the CNN and attention mechanism gave a good performance in handling tasks that require attention to important features. Although the training time was 163.34 s, which was slightly longer than CNN, the performance improvement proved the effectiveness of this combination in enhancing the model's predictive ability. (5) BiLSTM-AT model. This model's accuracy was 0.989, outperforming BiLSTM used alone. It combines the advantages of BiLSTM and the attention mechanism and performs well in tasks that require long-term dependencies and important feature attention. In addition, the model's training time was 1064.18 s. Although the performance improved, the training time was relatively long and unsuitable for fast iteration scenarios. (6) CNN-GRU model. The accuracy of this model was 0.982, which is an improvement compared with the standalone CNN model, but still lower than more complex structures such as CNN-BiLSTM and CNN-AT. Among 118 actual ST samples, the model had 12 prediction errors and a recall rate of 0.898, indicating that it still has some shortcomings in identifying ST companies. The training time was 418.58 s, which is at a moderate level. (7) Transformer model. The accuracy of this model was 0.992, ranking high among all models, second only to the CNN-BiLSTM-AT model. This model utilized a self-attention mechanism to model the relationships between time points in the sequence, effectively capturing the global dependency features of the data and improving overall prediction performance. However, when predicting ST samples, its performance was slightly inferior to CNN-BiLSTM-AT, indicating that sensitivity to small sample categories still needs to be strengthened. The training time was 151.87 s, one of the shortest among all models. (8) CNN-BiLSTM-AT model.