## 4 Result Analysis
### Experimental Settings  
#### 4.3.1 Evaluation Metrics  
In this section, the authors conducted two traffic prediction tasks and made predictions for different time lengths, respectively. The authors hoped to predict the traffic changes within the next day (short-term prediction) and the next week (long-term prediction) to cooperate with traffic scheduling, load balancing, and other means to achieve the health monitoring of the low Earth orbit constellation traffic system. By comparing our proposed model with other existing models, the excellent performance of the DST-LEO satellite-traffic prediction model was demonstrated. The evaluation metrics were the mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), and \(X_{i},\overset{\sim}{X_{i}}\) are the predicted values and the true values, respectively.  
\[\text{MSE} =\frac{1}{\text{m}}\text{$\sum_{i=1}^{\text{m}}$}\left(X_{i}- \overset{\sim}{X_{i}}\right)^{2} \tag{16}\] \[\text{RMSE} =\sqrt{\frac{1}{\text{m}}\text{$\sum_{i=1}^{\text{m}}$}\left(X_{ i}-\overset{\sim}{X_{i}}\right)^{2}}\] (17) \[\text{MAE} =\frac{1}{\text{m}}\text{$\sum_{i=1}^{\text{m}}$}\left|\left(X_{i }-\overset{\sim}{X_{i}}\right)\right|\] (18) \[\text{MAPE} =\frac{1}{\text{m}}\text{$\sum_{i=1}^{\text{m}}$}\left|\frac{X_{i }-\overset{\sim}{X_{i}}}{X_{i}}\right| \tag{19}\]  
The four metrics have different characteristics and functions. The mean squared error (MSE) amplifies the impact of large errors through the form of squared errors, so it is quite sensitive to outliers. In a network environment, significant traffic changes caused by sudden situations may have a large impact on the system, and MSE can help the model focus better on such situations. The root mean squared error (RMSE) is the square root of the MSE. It solves the problem of inconsistent dimensions in the MSE, making the result have the same dimension as the original data, which is convenient for observation, understanding, and interpretation. The mean absolute error (MAE) directly calculates the absolute error between the predicted value and the true value. It has a lower sensitivity to outliers and can more robustly reflect the overall performance of the model rather than just focusing on the prediction at a certain moment. The mean absolute percentage error (MAPE) represents the error in percentage form, which can intuitively reflect the proportion of the prediction error relative to the true value, facilitating cross-dataset comparison. However, when the true value is close to zero, the MAPE becomes unstable. Regardless of whether it is MSE, RMSE, MAE, or MAPE, the smaller their values, the higher the prediction accuracy of the model. By combining multiple metrics, the performance of the model can be evaluated more comprehensively.  
This paper did not evaluate the model in terms of the time complexity of training. Firstly, due to the fact that the dimensionality of this dataset was not large, the model's training time was insufficient to serve as an effective measurement indicator. Secondly, the four models had different numbers of training rounds. ARIMA only required 100 training rounds to achieve a good fitting effect, and the loss value no longer decreased. In contrast, the other models required 285 or 300 training rounds. Additionally, when conducting long-term prediction on the Abilene dataset, the LSTM-teacher forcing model required approximately 600 training rounds. Although the time complexity of training is also important, this paper paid more attention to the model's ability to capture traffic patterns to better predict future traffic.