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.
Principles and algorithms for forecasting groups of time series: Lo- cality and globality
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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.