{"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/mind-the-gap-an-experimental-evaluation-of","title":"Mind the gap: an experimental evaluation of imputation of missing values techniques in time series","arxiv_id":null,"date":"2020-01-15","proceeding":"Proceedings of the VLDB Endowment 2020 1","authors":["Mourad Khayati","Alberto Lerner","Zakhar Tymchenko","Philippe Cudré-Mauroux"],"abstract":"Recording sensor data is seldom a perfect process. Failures in power, communication or storage can leave occasional blocks of data missing, affecting not only real-time monitoring but also compromising the quality of near- and off-line data analysis. Several recovery (imputation) algorithms have been proposed to replace missing blocks. Unfortunately, little is known about their relative performance, as existing comparisons are limited to either a small subset of relevant algorithms or to very few datasets or often both. Drawing general conclusions in this case remains a challenge.\r\nIn this paper, we empirically compare twelve recovery algorithms using a novel benchmark. All but two of the algorithms were re-implemented in a uniform test environment. The benchmark gathers ten different datasets, which collectively represent a broad range of applications. Our benchmark allows us to fairly evaluate the strengths and weaknesses of each approach, and to recommend the best technique on a use-case basis. It also allows us to identify the limitations of the current body of algorithms and suggest future research directions.","url_abs":"https://dl.acm.org/doi/abs/10.14778/3377369.3377383","url_pdf":"http://www.vldb.org/pvldb/vol13/p768-khayati.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":"mind-the-gap-an-experimental-evaluation-of","repo_url":"https://github.com/eXascaleInfolab/bench-vldb20","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"imputation","task_name":"Imputation"},{"task_slug":"missing-values","task_name":"Missing Values"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}