{"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/estimating-time-varying-graphical-models","title":"Estimating Time-Varying Graphical Models","arxiv_id":"1804.03811","date":"2018-04-11","proceeding":null,"authors":["Jilei Yang","Jie Peng"],"abstract":"In this paper, we study time-varying graphical models based on data measured\nover a temporal grid. Such models are motivated by the needs to describe and\nunderstand evolving interacting relationships among a set of random variables\nin many real applications, for instance the study of how stocks interact with\neach other and how such interactions change over time.\n  We propose a new model, LOcal Group Graphical Lasso Estimation (loggle),\nunder the assumption that the graph topology changes gradually over time.\nSpecifically, loggle uses a novel local group-lasso type penalty to efficiently\nincorporate information from neighboring time points and to impose structural\nsmoothness of the graphs. We implement an ADMM based algorithm to fit the\nloggle model. This algorithm utilizes blockwise fast computation and\npseudo-likelihood approximation to improve computational efficiency. An R\npackage loggle has also been developed.\n  We evaluate the performance of loggle by simulation experiments. We also\napply loggle to S&P 500 stock price data and demonstrate that loggle is able to\nreveal the interacting relationships among stocks and among industrial sectors\nin a time period that covers the recent global financial crisis.","url_abs":"http://arxiv.org/abs/1804.03811v1","url_pdf":"http://arxiv.org/pdf/1804.03811v1.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":"abstracts"},"code_links":[{"paper_slug":"estimating-time-varying-graphical-models","repo_url":"https://github.com/jlyang1990/loggle","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"estimating-time-varying-graphical-models","repo_url":"https://github.com/jlyang1990/loggle_test","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"computational-efficiency","task_name":"Computational Efficiency"}],"methods":[{"method_slug":"admm","method_name":"ADMM"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}