{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T00:08:25Z","timestamp":1776125305498,"version":"3.50.1"},"reference-count":22,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,10,18]],"date-time":"2023-10-18T00:00:00Z","timestamp":1697587200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,10,18]],"date-time":"2023-10-18T00:00:00Z","timestamp":1697587200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,10,18]]},"DOI":"10.1109\/kse59128.2023.10299484","type":"proceedings-article","created":{"date-parts":[[2023,11,6]],"date-time":"2023-11-06T19:05:46Z","timestamp":1699297546000},"page":"1-6","source":"Crossref","is-referenced-by-count":9,"title":["Data Imputation for Multivariate Time-series Data"],"prefix":"10.1109","author":[{"given":"P. Le","family":"Lien","sequence":"first","affiliation":[{"name":"University of Medicine and Pharmacy at Ho Chi Minh City"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tu T.","family":"Do","sequence":"additional","affiliation":[{"name":"University of Science Vietnam National University Ho Chi Minh City"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thu","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Simula Metropolitan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref13","article-title":"Principle components analysis based frameworks for efficient missing data imputation algorithms","author":"nguyen","year":"2022","journal-title":"ArXiv Preprint"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1177\/1740774515602688"},{"key":"ref15","article-title":"Pmf: Efficient parameter estimation for data sets with missing data in some features","author":"nguyen","year":"0","journal-title":"Available at SSRN"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.108082"},{"key":"ref20","article-title":"Conditional expectation for missing data imputation","author":"anh vu","year":"2023","journal-title":"ArXiv Preprint"},{"key":"ref11","article-title":"OTexts","author":"hyndman","year":"2018","journal-title":"Forecasting Principles and Practice"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10804"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.2307\/1267352"},{"key":"ref21","author":"yoon","year":"2018","journal-title":"GAIN Missing Data Imputation using Generative Adversarial Nets"},{"key":"ref2","article-title":"Brits: Bidirectional recurrent imputation for time series","author":"cao","year":"2018","journal-title":"Advances in neural information processing systems"},{"key":"ref1","doi-asserted-by":"crossref","first-page":"66e","DOI":"10.2196\/resprot.6513","article-title":"Establishing linkages between distributed survey responses and consumer wearable device datasets: A pilot protocol","volume":"6","author":"julia","year":"2017","journal-title":"JMIR Res Protoc"},{"key":"ref17","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"pedregosa","year":"2011","journal-title":"Journal of Machine Learning Research"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-36819-6_3"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-49049-6_29"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-021-06083-7"},{"key":"ref8","first-page":"2016","author":"furberg","year":"2016","journal-title":"Crowd-sourced Fitbit datasets 03 12"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.119619"},{"key":"ref9","first-page":"3367","article-title":"Matrix completion and low-rank svd via fast alternating least squares","volume":"16","author":"hastie","year":"2015","journal-title":"The Journal of Machine Learning Research"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"ref3","author":"che","year":"2016","journal-title":"Recurrent neural networks for multivariate time series with missing values"},{"key":"ref6","article-title":"Pypots: A python toolbox for data mining on partially-observed time series","author":"wenjie","year":"2023","journal-title":"ArXiv Preprint"},{"key":"ref5","article-title":"Blockwise principal component analysis for monotone missing data imputation and dimensionality reduction","author":"tu","year":"2023","journal-title":"ArXiv Preprint"}],"event":{"name":"2023 15th International Conference on Knowledge and Systems Engineering (KSE)","location":"Hanoi, Vietnam","start":{"date-parts":[[2023,10,18]]},"end":{"date-parts":[[2023,10,20]]}},"container-title":["2023 15th International Conference on Knowledge and Systems Engineering (KSE)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10298834\/10298844\/10299484.pdf?arnumber=10299484","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,11]],"date-time":"2023-12-11T19:10:49Z","timestamp":1702321849000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10299484\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,18]]},"references-count":22,"URL":"https:\/\/doi.org\/10.1109\/kse59128.2023.10299484","relation":{},"subject":[],"published":{"date-parts":[[2023,10,18]]}}}