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

Abstract

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.

Extended abstract presented as a poster at the Northern Lights Deep Learning Conference 2024 in Tromsø, Norway