## 4 Experimental Results and Analysis
### Comparative Analysis of Experimental Results  
#### 4.4.1 Contrast Experiments  
Figure 4 is the experimental comparison point-line diagram, which shows the comparison among various models for various prediction performance indicators. The models covered in the figure include the LSTM, the BiLSTM, BiLSTM-Attention, CNN-LSTM, the CNN, CNN-BiLSTM-GRU-Attention, CNN-BiLSTM, LSTM-Attention, the GRU, CNN-Attention, and the FINM. In the form of a point-line diagram, the performance differences for each model in prediction performance indicators such as Loss Value, MAE, MSE, RMSE, MAPE, and R-Squared can be visually presented. Therefore, it can provide a key reference for the reasonable selection of appropriate models in relevant research or practical appli  
cation scenarios so as to weigh up the advantages and disadvantages of each model in different performance dimensions according to specific needs. Table 1 shows the prediction performance indicators of various models in comparative experiments, including Loss Value, MAE, MSE, RMSE, MAPE, R-Squared, training time/second, and prediction time/second. Among them, the LSTM Loss Value is 0.0164, indicating that the error between the predicted value and the true value of the model is small; the MAE value is 0.0968, which reflects the average absolute deviation between the predicted value and the actual value. MSE and RMSE both reflect the square of the prediction error, as RMSE is the square root of MSE, which is used to remain consistent with the original data unit; the MAPE value is 50.97, indicating the percentage of the prediction error relative to the actual value. R-Squared is 0.7736; the closer to 1, the better the model fitting effect. The training time/second and prediction time/second are relatively short, indicating that the model achieves a balance between efficiency and accuracy. iLSTM: Compared with the LSTM, MAE is slightly lower, indicating that the prediction error is reduced, and the training and prediction time is increased. Due to the computational complexity of bidirectional processing, the BiLSTM can capture the information \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model\(\backslash\)Indexes** & **Loss Value** & **MAE** & **MSE** & **RMSE** & **MAPE** & **R-Squared** & **Training** & **Prediction** \\  & & & & & & & & **Time/s** & **Time/s** \\ \hline LSTM & 0.0164 & 0.0968 & 0.0164 & 0.1269 & 50.97 & 0.7736 & 31.85 & **0.0606** \\ BiLSTM & 0.0164 & 0.0962 & 0.0164 & 0.1268 & 41.61 & 0.7741 & 44.31 & 0.0828 \\ BiLSTM-Attention & 0.0163 & 0.0975 & 0.0164 & 0.1269 & 26.99 & 0.7745 & 46.78 & 0.0906 \\ CNN-LSTM & 0.0163 & 0.0969 & 0.0163 & 0.1268 & 16.47 & 0.7742 & 96.77 & 0.1842 \\ CNN & 0.0163 & 0.0963 & 0.0163 & 0.1265 & 31.88 & 0.7757 & 42.97 & 0.0925 \\ CNN-BiLSTM-GRU-Attention & 0.0163 & 0.0959 & 0.0163 & 0.1267 & 52.23 & 0.7747 & 112.97 & 0.1983 \\ CNN-BiLSTM & 0.0162 & 0.0959 & 0.0161 & 0.1259 & 29.93 & 0.7777 & 167.31 & 0.2684 \\ LSTM-Attention & 0.0161 & 0.0963 & 0.0161 & 0.1260 & 31.33 & 0.7773 & 35.11 & 0.0867 \\ GRU & 0.0161 & 0.0962 & 0.0161 & 0.1259 & 46.14 & 0.7774 & **28.70** & 0.0617 \\ CNN-Attention & 0.0161 & 0.0957 & 0.0161 & 0.1258 & 20.21 & 0.7778 & 94.14 & 0.1194 \\ FINM & **0.0160** & **0.0947** & **0.0160** & **0.1255** & **18.40** & **0.7788** & 125.04 & 0.1882 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of predictive performance indicators in comparative experiments. Figure 4: Comparison of experimental point-line diagrams. before and after and is suitable for sequence tasks that require bidirectional understanding. BiLSTM-Attention: MAPE was significantly reduced to 26.99, indicating that the attention mechanism effectively improved the prediction accuracy, and the training and prediction time was further increased, but this was balanced by a higher prediction accuracy. The attention mechanism enables the model to focus on important parts of the input sequence and improve its prediction ability. CNN-LSTM: The MAPE value was 16.47, which is better, and the training time was longer.