Interpretable Nonroutine Network Traffic Prediction with a Case Study

TL;DR

This paper introduces a novel interpretable method for predicting bursty, nonroutine network traffic, validated through a soccer game case study, aiming to prevent large-scale network disruptions.

Contribution

The paper pioneers a nonroutine network traffic prediction method that balances interpretability, accuracy, and computational efficiency, with a case study validation.

Findings

Prediction closely fits real traffic patterns

Outperforms existing methods in interpretability and accuracy

Effective in anticipating bursty traffic events

Abstract

This paper pioneers a nonroutine network traffic prediction (NNTP) method to prospectively provide a theoretical basis for avoiding large-scale network disruption by accurately predicting bursty traffic. Certain events that impact user behavior subsequently trigger nonroutine traffic, which significantly constrains the performance of network traffic prediction (NTP) models. By analyzing nonroutine traffic and the corresponding events, the NNTP method is pioneered to construct interpretable NTP model. Based on the real-world traffic data, the network traffic generated during soccer games serves as a case study to validate the performance of the NNTP method. The numerical results indicate that our prediction closely fits the traffic pattern. In comparison to existing researches, the NNTP method is at the forefront of finding a balance among interpretability, accuracy, and computational complexity.