e2eTD forecasts a small set of aggregate series and disaggregates them via copula-based historical proportions, producing coherent probabilistic forecasts for huge retail hierarchies in minutes.
Principles and algorithms for forecasting groups of time series: Lo- cality and globality
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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.
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End-to-end probabilistic hierarchical forecasting of large hierarchies via probabilistic top-down
e2eTD forecasts a small set of aggregate series and disaggregates them via copula-based historical proportions, producing coherent probabilistic forecasts for huge retail hierarchies in minutes.
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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.