# A Long Short-Term Memory Recurrent Neural Network Framework for Network Traffic Matrix Prediction

arXiv: https://arxiv.org/abs/1705.05690 · HTML: https://mlsanity.com/paper/1705.05690/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2017.

## Built on by (most cited first, 8 of 8)

- [Review of Pedestrian Trajectory Prediction Methods: Comparing Deep Learning and Knowledge-based Approaches](https://mlsanity.com/paper/2111.06740/) (2021)
- [On the Limitations of Physics-informed Deep Learning: Illustrations Using First Order Hyperbolic Conservation Law-based Traffic Flow Models](https://mlsanity.com/paper/2302.12337/) (2023)
- [Adversarial Diffusion Attacks on Graph-based Traffic Prediction Models](https://mlsanity.com/paper/2104.09369/) (2021)
- [Listening to the city, attentively: A Spatio-Temporal Attention Boosted Autoencoder for the Short-Term Flow Prediction Problem](https://mlsanity.com/paper/2103.00983/) (2021)
- [pmSensing: A Participatory Sensing Network for Predictive Monitoring of Particulate Matter](https://mlsanity.com/paper/2111.11441/) (2021)
- [Time Series Prediction about Air Quality using LSTM-Based Models: A Systematic Mapping](https://mlsanity.com/paper/2111.11848/) (2021)
- [Learning to track environment state via predictive autoencoding](https://mlsanity.com/paper/2112.07745/) (2021)
- [VARADE: a Variational-based AutoRegressive model for Anomaly Detection on the Edge](https://mlsanity.com/paper/2409.14816/) (2024)

## Abstract (arXiv)

Network Traffic Matrix (TM) prediction is defined as the problem of estimating future network traffic 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 experience to classify, process and predict time series with time lags of unknown size. LSTMs have been shown to model temporal sequences and their long-range dependencies more accurately than conventional RNNs. In this paper, we propose a LSTM RNN framework for predicting short and long term Traffic Matrix (TM) in large networks. By validating our framework on real-world data from GEANT network, we show that our LSTM models converge quickly and give state of the art TM prediction performance for relatively small sized models.