Encoding Binary Events from Continuous Time Series in Rooted Trees using Contrastive Learning
Year: 2024
Type: preprint
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.
Source: arXiv (Cornell University)
Institution: Technical University of Denmark
Language: English
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4390572637