## 1 Introduction
These metrics were chosen to comprehensively evaluate the model's ability to handle both spatial and temporal data. While the 3D CNN is capable of learning temporal relationships between input images across different time steps, it primarily focuses on extracting spatial features. This limitation restricts its effectiveness in predicting long sequences with significant temporal characteristics. As a result, the authors' model is only capable of making accurate predictions for up to 60 min. This constraint arises because the 3D CNN is inherently designed to capture spatial patterns rather than long-term temporal dynamics, making it less suitable for extended period predictions and limiting its utility in scenarios requiring longer-term forecasting. In the prediction of communication satellite traffic demands, traditional methods have two significant drawbacks:  
1. In current communication satellite traffic demand prediction, large-scale statistical measurements of population characteristics in specific regions are required, without considering the prediction of needs for mobile terminals such as aviation and maritime ones, making it difficult to generalize to real systems [2; 3]. 2. In current communication satellite traffic demand prediction, the focus is mainly on predicting short-term traffic demands, with poor performance in long-term prediction [4]. Usually, time series prediction involves forecasting the observation value for the next time step, known as \"single-step prediction\", as only one time step is predicted at a time. However, for more complex time series problems, predicting multiple future time steps is often necessary, referred to as \"multi-step time series prediction\". The essence of demand prediction for satellite communication fundamentally requires a multi-step time series approach. This is the primary basis for proposing our model, which aims to overcome the limitations of single-step predictions. Unlike single-step prediction, which forecasts only the immediate next step, multi-step prediction provides a more comprehensive view of future demand, enabling better resource allocation and management in satellite communication. By incorporating multiple time steps into the prediction process, our model addresses the dynamic nature of satellite traffic and improves the accuracy and robustness of demand forecasting. Multi-step prediction models are particularly useful in satellite communication algorithms as they facilitate proactive planning and more effective management of varying traffic demands over time. Additionally, by predicting future traffic trends, multi-step models allow for the proactive allocation of satellite beams, thereby improving user satisfaction through better resource availability and service quality. The field of time series prediction has long been influenced by linear statistical methods such as ARIMA models. However, in the late 1970s and early 1980s, it became increasingly clear that linear models were inadequate for many practical applications [5]. Over the past twenty years, the rise in artificial intelligence has brought machine learning models into view and has become a powerful competitor to classic models in the prediction field [6]. Artificial neural networks (ANNs) are one of the widely used machine learning techniques for time series prediction. ANNs provide linear and nonlinear modeling without assuming the relationship between input and output variables. Therefore, artificial neural networks are more flexible and applicable than other methods [7]. There are various strategies such as direct prediction, recursive prediction, direct recursive fusion prediction, multiple output prediction, Seq2 Seq, etc., to address multi-step advance prediction tasks. These underlying model strategies can be applied to almost any prediction model [8].