  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.
