@article{Yang2026, 
author = {Xiaoxiong Yang and Yi Zhang and Dingde Jiang and Shuqing He},
title = {A Space-Air-Ground Integrated Network Traffic Estimation Algorithm Based on Time-Varying Higher-Order Moments},
year = {2026},
journal = {Computers, Materials & Continua},
volume = {88},
number = {2},
pages = {101},
keywords = {Traffic estimation, higher-order moments, non-stationary traffic, deep learning},
url = {https://www.sciopen.com/article/10.32604/cmc.2026.083723},
doi = {10.32604/cmc.2026.083723},
abstract = {With the proliferation of network users, traffic engineering has become increasingly important for the management and optimization of networks. As a crucial component of traffic engineering, the traffic matrix can assist network managers in making informed decisions to optimize resource utilization. However, in the current complex and heterogeneous space-ground integrated network, the cost of direct real-time measurement of traffic matrix is high and the delay is high. To address this challenge, we propose a network traffic estimation algorithm based on time-varying higher-order moments and deep learning, which leverages the time-varying higher-order moments property of traffic to improve the understanding of non-stationary traffic. First, we introduce an extended generalized autoregressive conditional heteroskedasticity model (THM-GARCH) that incorporates higher-order moment information to predict traffic volatility. Then, the THM-GARCH model is integrated with a long short-term memory network, and a dynamic feature update mechanism is developed to address the issue. The experimental results indicate that the proposed algorithm achieves the highest qualitative accuracy among all traffic estimation experiments, with a 17.78% reduction in root mean square error and a 14.69% reduction in mean square error.}
}