Identifying the root cause of cable network problems with machine learning

TL;DR

This paper explores machine learning techniques to identify and predict cable network problems in hybrid fiber coaxial networks, significantly improving fault detection precision and enabling predictive maintenance.

Contribution

It introduces an automated approach combining simple rules and advanced machine learning to enhance fault detection and forecasting in complex network topologies.

Findings

Precision@1 improved by 2.3 times using machine learning

Automated fault detection reduces manual troubleshooting

Forecasting enables proactive network maintenance

Abstract

Good quality network connectivity is ever more important. For hybrid fiber coaxial (HFC) networks, searching for upstream high noise in the past was cumbersome and time-consuming. Even with machine learning due to the heterogeneity of the network and its topological structure, the task remains challenging. We present the automation of a simple business rule (largest change of a specific value) and compare its performance with state-of-the-art machine-learning methods and conclude that the precision@1 can be improved by 2.3 times. As it is best when a fault does not occur in the first place, we secondly evaluate multiple approaches to forecast network faults, which would allow performing predictive maintenance on the network.