T-product factorization method for internet traffic data completion with spatio-temporal regularization

Recovery of network traffic data from incomplete observed data is an important issue in internet engineering and management. In this paper, by fully combining the temporal stability and periodicity features in internet traffic data, a new separable optimization model for internet data recovery is proposed, which is based upon the T-product factorization and the rapid discrete Fourier transform of tensors. The separable structural features presented in the model provide the possibility to design more efficient parallel algorithms. Moreover, by using generalized inverse matrices, an easy-to-operate and effective algorithm is proposed. In theory, we prove that under suitable conditions, every accumulation point of the sequence generated by the proposed algorithm is a stationary point of the established model. Numerical simulation results carried on the widely used real-world internet network datasets, show that the proposed method outperforms state-of-the-art competitions.