## 4 Result Analysis
### Experimental Settings
#### 4.3.2 Comparison with Existing Models  
ARIMA: ARIMA (autoregressive integrated moving average) [25] is a traditional time-series analysis method that is mainly used for modeling and predicting stationary time series. This model has strong capabilities in modeling linear relationships and can capture trends and time-period characteristics in a time series (for example, the traffic reaches a peak during the busy communication business hours in a day). Moreover, the model structure is relatively simple and highly interpretable. However, its ability to model complex nonlinear relationships is limited. By combining it with neural network models, its ability to model nonlinear relationships can be enhanced, enabling it to more comprehensively capture the features and patterns in time-series data and improve the accuracy of predictions.  
BiGRU-Attention [26]: Due to its simple structure, the gated recurrent unit (GRU) reduces computational complexity. When the amount of training data is small, GRU is less prone to overfitting than the long short-term memory (LSTM) network. Moreover, when storing model parameters and intermediate calculation results, GRU requires less memory space than LSTM. This model introduces a simple attention mechanism that mainly focuses on the information weighting in the feature dimension. It treats the input features as a whole. By calculating the importance weights of each feature vector, it performs a weighted sum on the feature vectors to highlight more important feature information. This mechanism does not take into account the temporal and spatial dimension characteristics of the data, and simply distributes attention from the perspective of features.  
LSTM-Teacher Forcing: Teacher forcing [27] is a training technique used when training recurrent neural networks (RNNs) and their variants (such as LSTM and GRU) for sequence generation tasks. In the traditional training method, the output of the model at the previous time step is used as the input for the next time step. However, this approach may lead to error accumulation. Once the model makes an incorrect prediction at a certain time step, subsequent predictions may be severely affected, making it difficult for the model to converge to good results. In the teacher-forcing mechanism, during the training process, with a certain probability, elements from the true target sequence are directly used as the input for the next time step, rather than using the model's own prediction result from the previous time step. This allows the model to learn the correct sequence patterns more quickly and speeds up the convergence process.  
The DST-LEO satellite-traffic prediction model demonstrated its advantages in handling low Earth orbit (LEO) constellation traffic prediction through a comparison with the aforementioned existing models. Although ARIMA is strong in handling the linear relationships in data, the traffic in LEO constellations is intricate, and inter-satellite links are affected by various factors. The comparison proved the advantages of recurrent neural network-like models. Since the LEO constellation supports an extremely large volume of services and the traffic amplitude changes significantly, a deviation in the prediction result at a certain moment will affect subsequent predictions. Therefore, the LSTM-Teacher Forcing mechanism provided the authors with new ideas for future research. The BiGRU-Attention model adopts a simple attention mechanism, while the authors' model introduced a spatio-temporal attention mechanism to separately process the traffic volume and link on-off data, aiming to capture the spatio-temporal dependencies and features in the data. The comparison proved the rationality and advantages of the spatio-temporal attention mechanism in the task of LEO constellation traffic prediction. Moreover, due to the large daily data volume of the constellation, LSTM has a more complex structure to learn the implicit patterns of traffic services, which is also one of the reasons why the authors chose LSTM over GRU.