# Multi-Step Traffic Prediction for Multi-Period Planning in Optical Networks

arXiv: https://arxiv.org/abs/2404.08314 · HTML: https://mlsanity.com/paper/2404.08314/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 13 references that are in the map.
- First posted on arXiv 12 Apr 2024.

## Builds on (most cited first, 2 of 2)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Survey on Machine Learning for Traffic-Driven Service Provisioning in Optical Networks](https://mlsanity.com/paper/2209.05080/) (2022)

## Abstract (arXiv)

A multi-period planning framework is proposed that exploits multi-step ahead traffic predictions to address service overprovisioning and improve adaptability to traffic changes, while ensuring the necessary quality-of-service (QoS) levels. An encoder-decoder deep learning model is initially leveraged for multi-step ahead prediction by analyzing real-traffic traces. This information is then exploited by multi-period planning heuristics to efficiently utilize available network resources while minimizing undesired service disruptions (caused due to lightpath re-allocations), with these heuristics outperforming a single-step ahead prediction approach.