# Improving Internet Traffic Matrix Prediction via Time Series Clustering

arXiv: https://arxiv.org/abs/2509.15072 · HTML: https://mlsanity.com/paper/2509.15072/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 28 references that are in the map.
- First posted on arXiv 18 Sep 2025.

## Builds on (most cited first, 4 of 4)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Principles and Algorithms for Forecasting Groups of Time Series: Locality and Globality](https://mlsanity.com/paper/2008.00444/) (2020)
- [NeuTM: A Neural Network-based Framework for Traffic Matrix Prediction in SDN](https://mlsanity.com/paper/1710.06799/) (2017)
- [Forecasting Across Time Series Databases using Recurrent Neural Networks on Groups of Similar Series: A Clustering Approach](https://mlsanity.com/paper/1710.03222/) (2017)

## Abstract (arXiv)

We present a novel framework that leverages time series clustering to improve internet traffic matrix (TM) prediction using deep learning (DL) models. Traffic flows within a TM often exhibit diverse temporal behaviors, which can hinder prediction accuracy when training a single model across all flows. To address this, we propose two clustering strategies, source clustering and histogram clustering, that group flows with similar temporal patterns prior to model training. Clustering creates more homogeneous data subsets, enabling models to capture underlying patterns more effectively and generalize better than global prediction approaches that fit a single model to the entire TM. Compared to existing TM prediction methods, our method reduces RMSE by up to 92% for Abilene and 75% for G'EANT. In routing scenarios, our clustered predictions also reduce maximum link utilization (MLU) bias by 18% and 21%, respectively, demonstrating the practical benefits of clustering when TMs are used for network optimization.