On the Role of Time Series Clustering in Traffic Matrix Prediction
For network operators needing accurate traffic matrix predictions, this work shows that a simple clustering-based decomposition can improve prediction accuracy over global models at low computational cost.
The paper investigates how clustering traffic flows into groups improves traffic matrix prediction, finding that clustering consistently outperforms global forecasting while being less costly than local prediction, with most gains achieved at moderate cluster numbers and similar RMSE across different clustering representations.
This paper analyzes the role of time-series clustering in traffic matrix (TM) prediction. Traffic flows within a TM often exhibit heterogeneous behavior, which can reduce the effectiveness of global forecasting models that predict all flows jointly. To address this, we propose a clustering-based prediction framework that groups flows into smaller subsets and trains separate predictors for each group. Four traffic-flow representations for clustering are explored, namely, histogram, autocorrelation function (ACF), power spectral density (PSD), and naïve partitioning, and how the representation choice and the number of clusters affect prediction performance. Experiments using the publicly available Abilene and GÉANT datasets show that clustering consistently improves over global forecasting baselines, while remaining substantially less costly than local prediction. The results further show that most of the performance gain is achieved at moderate values of K, with diminishing returns as the number of clusters increases. Although different clustering representations produce different partitions of the traffic flows, they often achieve similar root mean squared error (RMSE). This suggests that the main benefit of clustering lies in decomposing the TM prediction task into smaller subproblems, while the exact cluster structure plays a more limited role in determining overall prediction accuracy.