A Spatial-Temporal Traffic Prediction Algorithm Based on Non-Negative Tensor Factorization

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.