A tensor train approach for internet traffic data completion

Zhiyuan Zhang (), Chen Ling (), Hongjin He () and Liqun Qi ()
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Zhiyuan Zhang: Hangzhou Dianzi University
Chen Ling: Hangzhou Dianzi University
Hongjin He: Ningbo University
Liqun Qi: Hangzhou Dianzi University

Annals of Operations Research, 2024, vol. 339, issue 3, No 14, 1479 pages

Abstract: Abstract The internet traffic data completion is an important and challenging task in network engineering. Due to the multi-dimensionality of internet traffic data, we introduce two tensor train (TT) based optimization models with temporal regularization to recover the data from an incomplete observation. Moreover, we propose two easily implementable algorithms by following the spirit of alternating minimization. It is remarkable that our algorithms have closed-form solutions and one algorithm can be implemented in a parallel way for large-scale problems. Some numerical experiments on real-world datasets show that our approaches perform better than some existing state-of-the-art matrix- and tensor-based completion methods in terms of achieving higher accuracy and taking much less computing time for some datasets.

Keywords: Traffic matrix; Tensor completion; Tensor train decomposition; TT-rank; Singular value decomposition (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10479-021-04147-4

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