{"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/learning-localized-spatio-temporal-models","title":"Learning Localized Spatio-Temporal Models From Streaming Data","arxiv_id":"1802.03334","date":"2018-02-09","proceeding":"ICML 2018 7","authors":["Muhammad Osama","Dave Zachariah","Thomas B. Schön"],"abstract":"We address the problem of predicting spatio-temporal processes with temporal\npatterns that vary across spatial regions, when data is obtained as a stream.\nThat is, when the training dataset is augmented sequentially. Specifically, we\ndevelop a localized spatio-temporal covariance model of the process that can\ncapture spatially varying temporal periodicities in the data. We then apply a\ncovariance-fitting methodology to learn the model parameters which yields a\npredictor that can be updated sequentially with each new data point. The\nproposed method is evaluated using both synthetic and real climate data which\ndemonstrate its ability to accurately predict data missing in spatial regions\nover time.","url_abs":"http://arxiv.org/abs/1802.03334v2","url_pdf":"http://arxiv.org/pdf/1802.03334v2.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":"learning-localized-spatio-temporal-models","repo_url":"https://github.com/Muhammad-Osama/Localized-Spatio-temporal-Models","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"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}