{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/190503204","title":"Efficient On-line Computation of Visibility Graphs","arxiv_id":"1905.03204","date":"2019-05-08","proceeding":null,"authors":["Delia Fano Yela","Florian Thalmann","Vincenzo Nicosia","Dan Stowell","Mark Sandler"],"abstract":"A visibility algorithm maps time series into complex networks following a simple criterion. The resulting visibility graph has recently proven to be a powerful tool for time series analysis. However its straightforward computation is time-consuming and rigid, motivating the development of more efficient algorithms. Here we present a highly efficient method to compute visibility graphs with the further benefit of flexibility: on-line computation. We propose an encoder/decoder approach, with an on-line adjustable binary search tree codec for time series as well as its corresponding decoder for visibility graphs. The empirical evidence suggests the proposed method for computation of visibility graphs offers an on-line computation solution at no additional computation time cost. The source code is available online.","url_abs":"http://arxiv.org/abs/1905.03204v1","url_pdf":"http://arxiv.org/pdf/1905.03204v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"190503204","repo_url":"https://github.com/delialia/bst","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}