# NeuTM: A Neural Network-based Framework for Traffic Matrix Prediction in SDN

arXiv: https://arxiv.org/abs/1710.06799 · HTML: https://mlsanity.com/paper/1710.06799/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2017.

## Built on by (most cited first, 7 of 7)

- [Parameter-Efficient Quantum-Inspired Fast Weight Programmers for Traffic-Matrix Forecasting](https://mlsanity.com/paper/2606.27821/) (2026)
- [Multivariate Time Series characterization and forecasting of VoIP traffic in real mobile networks](https://mlsanity.com/paper/2307.06645/) (2023)
- [Diffusion Models Meet Network Management: Improving Traffic Matrix Analysis with Diffusion-based Approach](https://mlsanity.com/paper/2411.19493/) (2024)
- [Which Deep Learner? A Systematic Evaluation of Advanced Deep Forecasting Models Accuracy and Efficiency for Network Traffic Prediction](https://mlsanity.com/paper/2601.02694/) (2026)
- [5G Traffic Prediction with Time Series Analysis](https://mlsanity.com/paper/2110.03781/) (2021)
- [A Review of the Convergence of 5G/6G Architecture and Deep Learning](https://mlsanity.com/paper/2208.07643/) (2022)
- [Improving Internet Traffic Matrix Prediction via Time Series Clustering](https://mlsanity.com/paper/2509.15072/) (2025)

## Abstract (arXiv)

This paper presents NeuTM, a framework for network Traffic Matrix (TM) prediction based on Long Short-Term Memory Recurrent Neural Networks (LSTM RNNs). TM prediction is defined as the problem of estimating future network traffic matrix from the previous and achieved network traffic data. It is widely used in network planning, resource management and network security. Long Short-Term Memory (LSTM) is a specific recurrent neural network (RNN) architecture that is well-suited to learn from data and classify or predict time series with time lags of unknown size. LSTMs have been shown to model long-range dependencies more accurately than conventional RNNs. NeuTM is a LSTM RNN-based framework for predicting TM in large networks. By validating our framework on real-world data from GEEANT network, we show that our model converges quickly and gives state of the art TM prediction performance.