论文标题
通过机器学习确定有线网络问题的根本原因
Identifying the root cause of cable network problems with machine learning
论文作者
论文摘要
高质量的网络连接越来越重要。对于混合纤维同轴（HFC）网络，过去寻找上游高噪声是繁琐且耗时的。即使由于网络的异质性及其拓扑结构而引起的机器学习，该任务仍然具有挑战性。我们介绍了一个简单的业务规则（特定值的最大变化）的自动化，并将其性能与最新的机器学习方法进行比较，并得出结论，Precision@1可以提高2.3倍。由于最初没有发生故障时最好的是，我们第二次评估了多种预测网络故障的方法，这将允许在网络上执行预测性维护。
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.
