{"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/recurrent-neural-networks-for-multivariate","title":"Recurrent Neural Networks for Multivariate Time Series with Missing Values","arxiv_id":"1606.01865","date":"2016-06-06","proceeding":null,"authors":["Zhengping Che","Sanjay Purushotham","Kyunghyun Cho","David Sontag","Yan Liu"],"abstract":"Multivariate time series data in practical applications, such as health care,\ngeoscience, and biology, are characterized by a variety of missing values. In\ntime series prediction and other related tasks, it has been noted that missing\nvalues and their missing patterns are often correlated with the target labels,\na.k.a., informative missingness. There is very limited work on exploiting the\nmissing patterns for effective imputation and improving prediction performance.\nIn this paper, we develop novel deep learning models, namely GRU-D, as one of\nthe early attempts. GRU-D is based on Gated Recurrent Unit (GRU), a\nstate-of-the-art recurrent neural network. It takes two representations of\nmissing patterns, i.e., masking and time interval, and effectively incorporates\nthem into a deep model architecture so that it not only captures the long-term\ntemporal dependencies in time series, but also utilizes the missing patterns to\nachieve better prediction results. Experiments of time series classification\ntasks on real-world clinical datasets (MIMIC-III, PhysioNet) and synthetic\ndatasets demonstrate that our models achieve state-of-the-art performance and\nprovides useful insights for better understanding and utilization of missing\nvalues in time series analysis.","url_abs":"http://arxiv.org/abs/1606.01865v2","url_pdf":"http://arxiv.org/pdf/1606.01865v2.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":"recurrent-neural-networks-for-multivariate","repo_url":"https://github.com/WenjieDu/PyPOTS","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"recurrent-neural-networks-for-multivariate","repo_url":"https://github.com/Han-JD/GRU-D","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"recurrent-neural-networks-for-multivariate","repo_url":"https://github.com/Paul-Jacquit/Birds","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"recurrent-neural-networks-for-multivariate","repo_url":"https://github.com/PeterChe1990/GRU-D","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"recurrent-neural-networks-for-multivariate","repo_url":"https://github.com/edwinlebreton/Birds","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"recurrent-neural-networks-for-multivariate","repo_url":"https://github.com/fteufel/PyTorch-GRU-D","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"recurrent-neural-networks-for-multivariate","repo_url":"https://github.com/sindhura97/STraTS","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"imputation","task_name":"Imputation"},{"task_slug":"missing-values","task_name":"Missing Values"},{"task_slug":"multivariate-time-series-forecasting","task_name":"Multivariate Time Series Forecasting"},{"task_slug":"multivariate-time-series-imputation","task_name":"Multivariate Time Series Imputation"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"time-series-classification","task_name":"Time Series Classification"},{"task_slug":"time-series-prediction","task_name":"Time Series Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/multivariate-time-series-forecasting-on-1","task":"Multivariate Time Series Forecasting","dataset":"MuJoCo","model":"RNN GRU-D","rank_in_archive_order":4,"of":6,"metrics":{"MSE (10^-2, 50% missing)":"5.833"},"uses_additional_data":false},{"leaderboard":"/sota/multivariate-time-series-imputation-on-mujoco","task":"Multivariate Time Series Imputation","dataset":"MuJoCo","model":"RNN GRU-D","rank_in_archive_order":4,"of":6,"metrics":{"MSE (10^2, 50% missing)":"0.748"},"uses_additional_data":false},{"leaderboard":"/sota/time-series-classification-on-physionet","task":"Time Series Classification","dataset":"PhysioNet Challenge 2012","model":"GRU-D","rank_in_archive_order":8,"of":28,"metrics":{"AUC":"84.24%","AUC Stdev":"0.012%"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1606.01865","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1606.01865"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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