## 1 Introduction  
A schematic diagram of the communication services provided by a Low Earth Orbit (LEO) constellation [1] to users is shown in Figure 1. Terminals allow users to access satellite Internet services. The gateway station is responsible for the communication connection between satellites and the ground network. The ground control center ensures the normal operation of satellites and the quality of services. The services supported by this system generate a large amount of high-frequency data streams [2]. For example, in dynamic and dense environments (such as cities or disaster-stricken areas), user traffic fluctuates significantly, resulting in high-frequency traffic volatility. Regarding real-time data streams in applications like telemedicine, excessive delays in image transmission may affect the timeliness of diagnosis. In autonomous driving, data delays may impact the real-time control and safety. The LEO constellation can fulfill the task of providing crucial traffic transmission services with excellence, ensuring the timely delivery and processing of information. In order to achieve more efficient network management and optimal resource allocation, traffic prediction is of utmost importance. Traffic prediction for low Earth orbit (LEO) satellites is a crucial technology for maintaining network security and ensuring service quality that aims to predict the link traffic volume in future short- and long-term periods. Traditional methods, such as random forest, comprehensively predict the future traffic volume by integrating features from multiple decision trees including time-related features, basic traffic features, network device and topology features, and user behavior features. However, with the surge in traffic volume, the traffic in LEO constellation links is restricted by factors such as fault maintenance, bandwidth resources, and weather conditions. The inability to accurately predict may lead to irrational resource allocation [3], a decline in service quality, and an increase in operating costs. This poses challenges to ensuring the service quality of the services [4]. In the context of real-world low Earth orbit (LEO) constellation network traffic prediction, the impact of external factors on data transmission is crucial. Atmospheric conditions, electromagnetic interference, satellite equipment performance, inter-satellite link status, ground stations, and various other factors can all affect the communication quality to varying degrees. In terms of atmospheric conditions, for example, during periods of high solar activity, changes in the electron density in the ionosphere can affect signal propagation. Adverse weather conditions such as rain, snow, and fog can absorb and scatter signals, leading to signal attenuation. Regarding electromagnetic interference, solar flares can release large amounts of electromagnetic radiation, disrupting satellite communications. During wartime, human-induced interference from ground-based radar, microwave com  
Figure 1: Schematic diagram of the communication services provided to users by LEO constellations. munication devices, and other equipment can generate electromagnetic interference. The performance of the satellite equipment itself is also critical. Factors such as antenna gain, transmission power, and receiver sensitivity all impact normal communication. In terms of ground stations, the location of a ground station directly affects the visibility of satellites and the duration of communication while the network topology between ground stations and data centers influences the latency and reliability of data transmission. These external factors also present significant challenges in achieving accurate traffic prediction for LEO constellation networks. To solve the problem of accurately predicting traffic in future time periods, research on network traffic prediction technology has changed significantly over time.