The advancement of communication technology has made traffic engineering a critical issue in network systems. The traffic matrix is essential data that supports traffic engineering. The functionality of routing planning, network monitoring, and other modules within intelligent network management systems relies heavily on the network traffic matrix. However, real-time measurement of the network traffic matrix is costly and often suffers from missing or anomalous values. Consequently, long-term network traffic prediction presents significant challenges. Existing methods often fail to comprehensively address the multidimensional characteristics of traffic and the computational costs of the algorithms. To address these issues, we propose an efficient traffic prediction algorithm based on tensor factorization. First, we introduce a non-negative tensor factorization algorithm that accounts for link errors. This algorithm captures the spatial-temporal characteristics of traffic from different modes, thereby enhancing prediction efficiency. Next, we integrate the tensor factor matrix with a seasonal differential autoregressive moving average model in the temporal mode to identify traffic trends and complete the traffic prediction. Experimental results based on real data demonstrate that our algorithm performs exceptionally well in multi-step predictions and in capturing abnormal fluctuations.

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.