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Broadband infrastructure owners do not always know how their customers areconnected in the local networks, which are structured as rooted trees. A recentstudy is able to infer the topology of a local network using discrete timeseries data from the leaves of the tree (customers). In this study we propose acontrastive approach for learning a binary event encoder from continuous timeseries data. As a preliminary result, we show that our approach has somepotential in learning a valuable encoder.

Encoding Binary Events from Continuous Time Series in Rooted Trees using Contrastive Learning