## 1 Introduction
Introduced by [PERSON] and [PERSON] in 1997, LSTMs are a special type of RNN designed to handle long-term dependencies. Since their introduction, LSTMs have been improved and popularized by many researchers [15]. Almost all significant achievements based on RNNs are closely related to LSTMs, making them a focal point in deep learning. Researchers such as [PERSON], [PERSON], and the [PERSON] team have applied Seq2 Seq to extend Long Short-Term Memory (LSTM) networks, combining Seq2 Seq and LSTM. This approach demonstrates that predicting long time series is significantly more accurate than traditional techniques [16; 17; 18]. This advanced architecture is especially relevant for addressing the inherent challenges of modeling long-term dependencies in time series prediction. One significant application of this approach is in satellite communication systems, particularly for mobile terminals such as aviation and maritime ones. These systems experience significant variability in user \begin{table}
\begin{tabular}{c c c} \hline
**Algorithm** & **Advantages** & **Disadvantages** \\ \hline \multirow{6}{*}{Multi-step direct prediction} & \multicolumn{2}{c}{Lower computational efficiency when predicting multiple time steps (requires training multiple models for how many time steps to predict)} \\  & No error propagation & Higher prediction bias (because distant targets cannot obtain data close to them) [9; 10] \\ \hline \multirow{6}{*}{Multi-step recursive prediction} & Fixed number of models required & Prediction error amplifies with time propagation due to using the previous step’s prediction as input, resulting in higher prediction variance [9; 10] \\  & Lower modulation error compared to multi-step direct prediction & Prediction error amplifies with time propagation due to using the previous step’s prediction as input, resulting in higher prediction variance [9; 10] \\ \hline \multirow{6}{*}{Direct and recursive fusion prediction} & Combining recursive prediction leads to lower prediction bias & \multirow{2}{*}{\begin{tabular}{c} High computational complexity \\ High implementation complexity [9] \\ Performance is always better than the first two \\ \end{tabular} } \\  & \begin{tabular}{c} Combining direct prediction, reducing \\ prediction variance \\ Performance is always better than the first two \\ \end{tabular} & \multirow{2}{*}{\begin{tabular}{c} High computational complexity \\ High implementation complexity [9] \\ Performance is always better than the first two \\ \end{tabular} } \\ \hline \multirow{6}{*}{Multi-output prediction} & Predictions for subsequent time steps and the previous time step are independent, avoiding error accumulation & Does not consider sequence dependencies \\  & Learning multiple outputs & High computational complexity [9] \\  & simultaneously, identifying correlations & \\  & for each time step & \\ \hline \multirow{6}{*}{Seq2 Seq model} & Better at handling long sequences, can & \multirow{2}{*}{\
\begin{tabular}{c} Long training time \\ Requires high-quality training data [11] \\ \end{tabular} } \\  &
\begin{tabular}{c} handle input and output \\ sequences of different lengths \\ Can be combined with deep learning \\ \end{tabular} & & \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of Classic Models for Multi-step Time Series. demand, and traditional methods often struggle with long-term prediction and adaptation to rapidly changing conditions. The primary issue addressed in this study is the challenge of accurately predicting dynamically changing traffic demands in such environments. This research develops a robust traffic demand prediction method that improves resource management efficiency in satellite communication systems. A novel deep learning-based approach is proposed, combining Sequence-to-Sequence (Seq2 Seq) and Long Short-Term Memory (LSTM) networks to enhance prediction accuracy and resource management.