使用机器学习识别电缆网络问题的根本原因
Identifying the root cause of cable network problems with machine learning
摘要：
高质量的网络连接变得越来越重要。对于混合光纤同轴（HFC）网络，搜索上游高噪声在过去是繁琐和耗时的。即使使用机器学习，由于网络及其拓扑结构的异构性，任务仍然具有挑战性。我们提出了一个简单的业务规则（特定值的最大变化）的自动化，并将其性能与最先进的机器学习方法进行了比较，得出的结论是，精度@1可以提高2.3倍。由于最好的情况是首先不发生故障，我们其次评估了多种方法来预测网络故障，这将允许对网络进行预测性维护。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
