Clustering traffic flows with histogram, ACF, PSD or naive representations improves traffic matrix prediction over global models on Abilene and GÉANT data, with most gains at moderate cluster counts and similar accuracy across representations.
Towards traffic matrix prediction with lstm recurrent neural networks,
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On the Role of Time Series Clustering in Traffic Matrix Prediction
Clustering traffic flows with histogram, ACF, PSD or naive representations improves traffic matrix prediction over global models on Abilene and GÉANT data, with most gains at moderate cluster counts and similar accuracy across representations.