Encoding Binary Events from Continuous Time Series in Rooted Trees using
  Contrastive Learning
Tobias Engelhardt Rasmussen Siv Sørensen
Broadband infrastructure owners do not always know how their customers are
connected in the local networks, which are structured as rooted trees. A recent
study is able to infer the topology of a local network using discrete time
series data from the leaves of the tree (customers). In this study we propose a
contrastive approach for learning a binary event encoder from continuous time
series data. As a preliminary result, we show that our approach has some
potential in learning a valuable encoder.