Multi-step Traffic Prediction Based on Causal Structure Learning and Improved Seq2Seq for LEO Satellite Networks
Liang Peng, Jie Yan, Xiaoxiang Wang · 2024
The rapid movement of low Earth orbit (LEO) satellites leads to dynamic changes in their ground access traffic. Multi-step traffic prediction can sense long-term changes in traffic, and accurate traffic prediction is essential for analyzing satellite traffic loads. In this paper, we propose a multi-step traffic prediction (MTP-CS) model based on causal structure learning and improved sequence-to-sequence (Seq2Seq) to improve traffic prediction accuracy by fully exploiting the spatio-temporal relationship of satellite traffic. Specifically, we introduce causal structure learning to model the spatial relationship between satellite traffic as a causal graph and utilize graph attention network (GAT) to aggregate spatial features. Then, we propose an improved Seq2Seq (Seq2Seq-DCCN) by constructing its encoder using a dilated causal convolutional network (DCCN), which can capture the long-term temporal relationships of traffic sequences. Experiments on real traffic datasets show that the proposed MTPCS model achieves 16.59% to 21.9% error reduction compared to the baseline model regarding root mean square error (RMSE) metric at different prediction steps.