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.
Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach,
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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.