@Article{cmc.2026.083723,
AUTHOR = {Xiaoxiong Yang, Yi Zhang, Dingde Jiang, Shuqing He},
TITLE = {A Space-Air-Ground Integrated Network Traffic Estimation Algorithm Based on Time-Varying Higher-Order Moments},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/cmc/online/detail/27154},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2026.083723}
}