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A tensor train approach for internet traffic data completion
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DOI：10.1007/s10479-021-04147-4.png)
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Abstract
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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
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