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A tensor train approach for internet traffic data completion

Author

Listed:
  • Zhiyuan Zhang

    (Hangzhou Dianzi University)

  • Chen Ling

    (Hangzhou Dianzi University)

  • Hongjin He

    (Ningbo University)

  • Liqun Qi

    (Hangzhou Dianzi University)

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.

Suggested Citation

  • Zhiyuan Zhang & Chen Ling & Hongjin He & Liqun Qi, 2024. "A tensor train approach for internet traffic data completion," Annals of Operations Research, Springer, vol. 339(3), pages 1461-1479, August.
    • Handle: RePEc:spr:annopr:v:339:y:2024:i:3:d:10.1007_s10479-021-04147-4
    DOI: 10.1007/s10479-021-04147-4
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    References listed on IDEAS

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