## 1 Introduction
Unlike typical Seq2 Seq applications focused on natural language processing or image processing, this approach applies Seq2 Seq structures to predict satellite communication traffic, modeling complex spatiotemporal patterns unique to satellite networks, including variations due to user mobility and geographical distribution. This approach introduces several innovative elements to tackle the specific challenges of traffic demand prediction in satellite communication:  
1. Development of a Traffic Demand Prediction Method for Mobile Terminals:  
Traditional prediction models struggle with the dynamic and non-stationary nature of data from mobile terminals, such as those used in aviation and maritime sectors. This method is particularly suitable for these mobile terminals, ensuring accurate predictions despite the challenges, thereby improving the ability to predict traffic demand in highly variable environments. 2. Generalization to Real-World Systems without Extensive Statistical Data:  
Traditional methods often require large-scale statistical datasets for accurate predictions, limiting their applicability. This method is designed to work effectively with limited data, making it highly generalizable and applicable to various real-world systems. This overcomes a significant limitation of current approaches and enhances the prediction accuracy in satellite communication systems with limited data availability. 1. Stable Long-Term Traffic Demand Predictions:  
Traditional models often suffer from reduced accuracy and increased volatility over extended prediction horizons. This approach ensures consistent performance, enhancing the reliability of traffic demand forecasts over the long term. This stability improves resource management by allowing more accurate long-term predictions in satellite communication networks. The primary contribution of this study is the introduction of a novel traffic demand prediction method tailored for satellite communication systems. This method stands out from existing techniques due to its ability to provide higher prediction accuracy and its adaptability to a wide range of practical scenarios, particularly for mobile terminals. By not relying on extensive statistical data, the approach is versatile and can be implemented across various systems to handle long-term time series prediction. This characteristic is a significant advantage in practical applications, where data availability and quality may be limited. Moreover, the approach effectively addresses the shortcomings of existing methods in long-term prediction, which often struggle with maintaining stability and accuracy over extended periods. By enabling stable and accurate long-term predictions, this method significantly enhances resource management efficiency and effectiveness in satellite communication networks. This is a critical improvement, as it allows for the better allocation of resources and improved service quality in dynamic environments. The remaining sections are structured as follows: Section 2 introduces the detailed structure of the traffic prediction model, covering the Seq2 Seq framework combined with LSTM networks and aviation demand modeling, laying the groundwork for the following algorithmic steps. Section 3 outlines the steps for predicting traffic demands, including data preprocessing, model training, and parameter tuning, building on the model structure from Section 2. Section 4 presents simulation results, comparing the model's effectiveness and stability against traditional methods, setting the stage for the summary of key findings. Finally, Section 5 summarizes key findings, practical benefits for satellite communication systems, and potential future research directions, tying together insights from the previous sections.