{"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-missing-data-in-temporal-data","title":"Estimating Missing Data in Temporal Data Streams Using Multi-directional Recurrent Neural Networks","arxiv_id":"1711.08742","date":"2017-11-23","proceeding":null,"authors":["Jinsung Yoon","William R. Zame","Mihaela van der Schaar"],"abstract":"Missing data is a ubiquitous problem. It is especially challenging in medical\nsettings because many streams of measurements are collected at different - and\noften irregular - times. Accurate estimation of those missing measurements is\ncritical for many reasons, including diagnosis, prognosis and treatment.\nExisting methods address this estimation problem by interpolating within data\nstreams or imputing across data streams (both of which ignore important\ninformation) or ignoring the temporal aspect of the data and imposing strong\nassumptions about the nature of the data-generating process and/or the pattern\nof missing data (both of which are especially problematic for medical data). We\npropose a new approach, based on a novel deep learning architecture that we\ncall a Multi-directional Recurrent Neural Network (M-RNN) that interpolates\nwithin data streams and imputes across data streams. We demonstrate the power\nof our approach by applying it to five real-world medical datasets. We show\nthat it provides dramatically improved estimation of missing measurements in\ncomparison to 11 state-of-the-art benchmarks (including Spline and Cubic\nInterpolations, MICE, MissForest, matrix completion and several RNN methods);\ntypical improvements in Root Mean Square Error are between 35% - 50%.\nAdditional experiments based on the same five datasets demonstrate that the\nimprovements provided by our method are extremely robust.","url_abs":"http://arxiv.org/abs/1711.08742v1","url_pdf":"http://arxiv.org/pdf/1711.08742v1.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-missing-data-in-temporal-data","repo_url":"https://github.com/jsyoon0823/MRNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"estimating-missing-data-in-temporal-data","repo_url":"https://github.com/WenjieDu/PyPOTS","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"estimating-missing-data-in-temporal-data","repo_url":"https://github.com/WenjieDu/SAITS","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"matrix-completion","task_name":"Matrix Completion"},{"task_slug":"multivariate-time-series-imputation","task_name":"Multivariate Time Series Imputation"},{"task_slug":"prognosis","task_name":"Prognosis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/multivariate-time-series-imputation-on","task":"Multivariate Time Series Imputation","dataset":"Beijing Multi-Site Air-Quality Dataset","model":"M-RNN","rank_in_archive_order":4,"of":6,"metrics":{"MAE (PM2.5)":"14.24"},"uses_additional_data":false},{"leaderboard":"/sota/multivariate-time-series-imputation-on-1","task":"Multivariate Time Series Imputation","dataset":"PhysioNet Challenge 2012","model":"M-RNN","rank_in_archive_order":4,"of":9,"metrics":{"MAE (10% of data as GT)":"0.451"},"uses_additional_data":false},{"leaderboard":"/sota/multivariate-time-series-imputation-on-uci","task":"Multivariate Time Series Imputation","dataset":"UCI localization data","model":"M-RNN","rank_in_archive_order":2,"of":5,"metrics":{"MAE (10% missing)":"0.248"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1711.08742","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}