  Loss of Signal (LOS) represents a significant cost for operators of optical
networks. By studying large sets of real-world Performance Monitoring (PM) data
collected from six international optical networks, we find that it is possible
to forecast LOS events with good precision 1-7 days before they occur, albeit
at relatively low recall, with supervised machine learning (ML). Our study
covers twelve facility types, including 100G lines and ETH10G clients. We show
that the precision for a given network improves when training on multiple
networks simultaneously relative to training on an individual network.
Furthermore, we show that it is possible to forecast LOS from all facility
types and all networks with a single model, whereas fine-tuning for a
particular facility or network only brings modest improvements. Hence our ML
models remain effective for optical networks previously unknown to the model,
which makes them usable for commercial applications.
