{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T19:41:44Z","timestamp":1780515704563,"version":"3.54.1"},"reference-count":216,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U21A20478"],"award-info":[{"award-number":["U21A20478"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zhejiang High-Level Talents Special Support Program","award":["2021R52002"],"award-info":[{"award-number":["2021R52002"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LZ21F030004"],"award-info":[{"award-number":["LZ21F030004"]}]},{"DOI":"10.13039\/100022963","name":"Key Research and Development Program of Zhejiang Province","doi-asserted-by":"publisher","award":["2025C01061"],"award-info":[{"award-number":["2025C01061"]}],"id":[{"id":"10.13039\/100022963","id-type":"DOI","asserted-by":"publisher"}]},{"name":"State Key Laboratory of Industrial Control Technology Project","award":["ICT2025A09"],"award-info":[{"award-number":["ICT2025A09"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Commun. Surv. Tutorials"],"published-print":{"date-parts":[[2026]]},"DOI":"10.1109\/comst.2025.3585962","type":"journal-article","created":{"date-parts":[[2025,7,4]],"date-time":"2025-07-04T13:53:43Z","timestamp":1751637223000},"page":"149-180","source":"Crossref","is-referenced-by-count":8,"title":["Missing Data Recovery Methods on Multivariate Time Series in IoT: A Comprehensive Survey"],"prefix":"10.1109","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9031-238X","authenticated-orcid":false,"given":"Kai","family":"Zhang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1602-8986","authenticated-orcid":false,"given":"Qinmin","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8886-1547","authenticated-orcid":false,"given":"Chao","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, and the International Business School, Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7635-8141","authenticated-orcid":false,"given":"Xin","family":"Sun","sequence":"additional","affiliation":[{"name":"International Business School, Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3155-3145","authenticated-orcid":false,"given":"Jiming","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"issue":"3","key":"ref1","first-page":"88","article-title":"Pattern recognition in multivariate time series: Towards an automated event detection method for smart manufacturing systems","volume":"4","author":"Kapp","year":"2020","journal-title":"J. Manuf. Mater. Process."},{"issue":"1","key":"ref2","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1109\/TASE.2020.3026065","article-title":"Process monitoring and fault prediction in multivariate time series using bag-of-words","volume":"19","author":"Guo","year":"2022","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref3","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.enbuild.2019.05.021","article-title":"A methodology for energy multivariate time series forecasting in smart buildings based on feature selection","volume":"196","author":"Gonzalez-Vidal","year":"2019","journal-title":"Energy Build."},{"key":"ref4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ins.2020.02.069","article-title":"TSI: Time series to imaging based model for detecting anomalous energy consumption in smart buildings","volume":"523","author":"Fahim","year":"2020","journal-title":"Inf. Sci."},{"key":"ref5","doi-asserted-by":"crossref","first-page":"211490","DOI":"10.1109\/ACCESS.2020.3039733","article-title":"Deep neural networks for multivariate prediction of photovoltaic power time series","volume":"8","author":"Succetti","year":"2020","journal-title":"IEEE Access"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.epsr.2016.07.018","article-title":"Classification for consumption data in smart grid based on forecasting time series","volume":"141","author":"Tornai","year":"2016","journal-title":"Electr. Power Syst. Res."},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1186\/2193-1801-2-222"},{"issue":"3","key":"ref8","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.jkss.2018.03.002","article-title":"Bayesian methods for dealing with missing data problems","volume":"47","author":"Ma","year":"2018","journal-title":"J. Korean Stat. Soc."},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-009-0295-6"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-011-0424-2"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"63279","DOI":"10.1109\/ACCESS.2018.2877269","article-title":"A survey on data imputation techniques: Water distribution system as a use case","volume":"6","author":"Osman","year":"2018","journal-title":"IEEE Access"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.advwatres.2017.10.002","article-title":"Multivariate missing data in hydrology\u2014Review and applications","volume":"110","author":"Aissia","year":"2017","journal-title":"Adv. Water Resource"},{"issue":"12","key":"ref13","first-page":"3861","article-title":"Roles of imputation methods for filling the missing values: A review","volume":"7","author":"Ramli","year":"2013","journal-title":"Adv. Environ. Biol."},{"key":"ref14","doi-asserted-by":"crossref","DOI":"10.1016\/j.oceaneng.2020.108261","article-title":"Real-time data-driven missing data imputation for short-term sensor data of marine systems. A comparative study","volume":"218","author":"Velasco-Gallego","year":"2020","journal-title":"Ocean Eng."},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-019-09709-4"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-15-1275-9_25"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/3533381"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.2307\/2335739"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3010524"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2018.2847660"},{"key":"ref21","volume-title":"Applied Missing Data Analysis","author":"Enders","year":"2022"},{"key":"ref22","volume-title":"Data Analysis Using Regression and Multilevel\/Hierarchical Models","author":"Gelman","year":"2007"},{"key":"ref23","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-030-01180-2","volume-title":"Deep Learning and Missing Data in Engineering Systems","author":"Leke","year":"2019"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1002\/9781119013563"},{"key":"ref25","doi-asserted-by":"crossref","DOI":"10.1201\/9781439821862","volume-title":"Analysis of Incomplete Multivariate Data","author":"Schafer","year":"1997"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1002\/9780470316696"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1037\/\/1082-989X.7.2.147"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclinepi.2006.01.014"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.psych.58.110405.085530"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsp.2009.10.001"},{"key":"ref31","doi-asserted-by":"crossref","first-page":"1866","DOI":"10.1109\/ACCESS.2019.2962152","article-title":"Time series data cleaning: A survey","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-16-2248-9_42"},{"key":"ref33","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.neucom.2021.02.046","article-title":"A review of irregular time series data handling with gated recurrent neural networks","volume":"441","author":"Weerakody","year":"2021","journal-title":"Neurocomputing"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2023.3271419"},{"key":"ref35","article-title":"Time series data imputation: A survey on deep learning approaches","author":"Fang","year":"2020","journal-title":"arXiv:2011.11347"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3116785"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICDIM.2018.8846984"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1002\/9781118029145"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2016.04.015"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2015.02.050"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2020.3009265"},{"key":"ref42","article-title":"Comparison of different methods for univariate time series imputation in R","author":"Moritz","year":"2015","journal-title":"arXiv:1510.03924"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/ICoAC.2014.7229721"},{"key":"ref44","volume-title":"Gaussian Processes for Regression: A Quick Introduction","author":"Ebden","year":"2008"},{"issue":"4","key":"ref45","doi-asserted-by":"crossref","first-page":"783","DOI":"10.2307\/2528820","article-title":"The analysis of incomplete data","volume":"27","author":"Hartley","year":"1971","journal-title":"Biometrics"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1080\/00401706.1980.10486171"},{"key":"ref47","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1016\/j.asoc.2018.07.027","article-title":"Wind power prediction with missing data using gaussian process regression and multiple imputation","volume":"71","author":"Liu","year":"2018","journal-title":"Appl. Soft Comput."},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1080\/10618600.2016.1152970"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.2307\/2984875"},{"key":"ref50","first-page":"1","article-title":"Supervised learning from incomplete data via an EM approach","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"6","author":"Ghahramani"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2011.2168823"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2014.2336635"},{"key":"ref53","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.atmosenv.2014.11.049","article-title":"Imputation of missing data in time series for air pollutants","volume":"102","author":"Junger","year":"2015","journal-title":"Atmosph. Environ."},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.03.097"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1002\/9780470191613"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1137\/1026034"},{"issue":"3","key":"ref57","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1016\/S0167-9473(02)00163-9","article-title":"Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models","volume":"41","author":"Biernacki","year":"2003","journal-title":"Comput. Stat. Data Anal."},{"issue":"3","key":"ref58","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1016\/S0167-9473(02)00177-9","article-title":"Choosing initial values for the EM algorithm for finite mixtures","volume":"41","author":"Karlis","year":"2003","journal-title":"Comput. Stat. Data Anal."},{"key":"ref59","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.arcontrol.2018.09.003","article-title":"Review and big data perspectives on robust data mining approaches for industrial process modeling with outliers and missing data","volume":"46","author":"Zhu","year":"2018","journal-title":"Annu. Rev. Control"},{"issue":"6","key":"ref60","doi-asserted-by":"crossref","first-page":"1554","DOI":"10.1214\/aoms\/1177699147","article-title":"Statistical inference for probabilistic functions of finite state Markov chains","volume":"37","author":"Baum","year":"1966","journal-title":"Ann. Math. Stat."},{"issue":"1","key":"ref61","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1214\/aoms\/1177697196","article-title":"A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains","volume":"41","author":"Baum","year":"1970","journal-title":"Ann. Math. Stat."},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671760"},{"issue":"1","key":"ref63","doi-asserted-by":"crossref","DOI":"10.1016\/j.jeconom.2023.05.005","article-title":"High-dimensional conditionally gaussian state space models with missing data","volume":"236","author":"Chan","year":"2023","journal-title":"J. Econom."},{"key":"ref64","first-page":"10334","article-title":"Factorized inference in deep Markov models for incomplete multimodal time series","volume-title":"Proc. AAAI Conf. Artif. Intell.","volume":"34","author":"Zhi-Xuan"},{"key":"ref65","article-title":"State space model multiple imputation for missing data in non-stationary multivariate time series with application in digital psychiatry","author":"Cai","year":"2023","journal-title":"arXiv:2206.14343"},{"key":"ref66","first-page":"1","article-title":"Support vector regression machines","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"9","author":"Drucker"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1023\/a:1010933404324"},{"issue":"3","key":"ref68","first-page":"18","article-title":"Classification and regression by RandomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"issue":"5","key":"ref69","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref70","first-page":"1","article-title":"LightGBM: A highly efficient gradient boosting decision tree","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Ke"},{"issue":"8","key":"ref71","doi-asserted-by":"crossref","first-page":"2933","DOI":"10.1109\/TITS.2018.2869768","article-title":"Missing value imputation for traffic-related time series data based on a multi-view learning method","volume":"20","author":"Li","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2013.01.021"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1016\/j.cjche.2022.01.033"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1615\/JMachLearnModelComput.2021038774"},{"key":"ref75","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.124016","article-title":"Meta-learning for vessel time series data imputation method recommendation","volume":"251","author":"Fatyanosa","year":"2024","journal-title":"Exp. Syst. Appl."},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-023-04828-6"},{"key":"ref77","volume-title":"Time Series Analysis: Forecasting and Control","author":"Box","year":"2015"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1177\/0013164404272502"},{"issue":"4","key":"ref79","doi-asserted-by":"crossref","first-page":"1042","DOI":"10.1109\/TCBB.2022.3205064","article-title":"Time-aware missing healthcare data prediction based on ARIMA model","volume":"21","author":"Kong","year":"2024","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinf."},{"key":"ref80","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.eswa.2019.03.044","article-title":"A bagging algorithm for the imputation of missing values in time series","volume":"129","author":"Andiojaya","year":"2019","journal-title":"Exp. Syst. Appl."},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1007\/bf00153759"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1023\/a:1007626913721"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1967.1053964"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1145\/3077584.3077592"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1109\/QRS.2016.20"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2007.01.008"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2014.09.038"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2914653"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2022.3230698"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2006.871582"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1109\/LCOMM.2015.2489212"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2017.06.010"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.1155\/2015\/364089"},{"issue":"8","key":"ref94","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/MC.2009.263","article-title":"Matrix factorization techniques for recommender systems","volume":"42","author":"Koren","year":"2009","journal-title":"Computer"},{"key":"ref95","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2013.6638314"},{"key":"ref96","doi-asserted-by":"publisher","DOI":"10.1145\/3144457.3144474"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390267"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33786-4_10"},{"key":"ref99","first-page":"1512","article-title":"Probabilistic matrix factorization with non-random missing data","volume-title":"Proc. 31st Int. Conf. Mach. Learn.","author":"Hernandez-Lobato"},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2010.5540139"},{"key":"ref101","first-page":"19","article-title":"Large-scale matrix factorization with missing data under additional constraints}","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"23","author":"Mitra"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107114"},{"key":"ref103","doi-asserted-by":"publisher","DOI":"10.1109\/CAC51589.2020.9327323"},{"issue":"22","key":"ref104","doi-asserted-by":"crossref","first-page":"13491","DOI":"10.1109\/JSEN.2020.3004186","article-title":"A novel approach based on matrix factorization for recovering missing time series sensor data","volume":"20","author":"Song","year":"2020","journal-title":"IEEE Sensors J."},{"key":"ref105","doi-asserted-by":"crossref","DOI":"10.1016\/j.ecoinf.2022.101775","article-title":"Deep matrix factorization models for estimation of missing data in a low-cost sensor network to measure air quality","volume":"71","author":"Rivera-Mu\u00f1oz","year":"2022","journal-title":"Ecol. Inf."},{"key":"ref106","doi-asserted-by":"publisher","DOI":"10.1145\/3443467.3443746"},{"key":"ref107","doi-asserted-by":"publisher","DOI":"10.1098\/rspa.1998.0193"},{"key":"ref108","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2012.6288750"},{"key":"ref109","doi-asserted-by":"publisher","DOI":"10.1186\/s40064-016-3692-1"},{"key":"ref110","doi-asserted-by":"publisher","DOI":"10.1177\/1475921720932813"},{"key":"ref111","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2013.2288675"},{"issue":"1","key":"ref112","first-page":"3","article-title":"STL: A seasonal-trend decomposition","volume":"6","author":"Cleveland","year":"1990","journal-title":"J. Official Stat."},{"key":"ref113","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2017.07.007"},{"key":"ref114","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.2970467"},{"key":"ref115","doi-asserted-by":"publisher","DOI":"10.1109\/ICSAI.2014.7009332"},{"key":"ref116","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-24271-9"},{"key":"ref117","first-page":"6776","article-title":"BRITs: Bidirectional recurrent imputation for time series","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Cao"},{"key":"ref118","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2018.2874712"},{"key":"ref119","article-title":"Multiple imputation using deep denoising autoencoders","author":"Gondara","year":"2017","journal-title":"arXiv:1705.02737"},{"key":"ref120","first-page":"4413","article-title":"MIWAE: Deep generative modelling and imputation of incomplete data sets","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Mattei"},{"key":"ref121","doi-asserted-by":"publisher","DOI":"10.1016\/j.ifacol.2018.09.406"},{"key":"ref122","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107501"},{"key":"ref123","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3186498"},{"key":"ref124","article-title":"A convolution recurrent autoencoder for spatio-temporal missing data imputation","author":"Asadi","year":"2019","journal-title":"arXiv:1904.12413"},{"key":"ref125","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2022.3167995"},{"key":"ref126","first-page":"1651","article-title":"GP-VAE: Deep probabilistic time series imputation}","volume-title":"Proc. 23rd Int. Conf. Artif. Intell. Stat.","author":"Fortuin"},{"key":"ref127","article-title":"Multi-task sequence to sequence learning","author":"Luong","year":"2015","journal-title":"arXiv:1511.06114"},{"key":"ref128","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref129","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2909038"},{"key":"ref130","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2021.106377"},{"key":"ref131","article-title":"Empirical evaluation of gated recurrent neural networks on sequence modeling","author":"Chung","year":"2014","journal-title":"arXiv:1412.3555"},{"key":"ref132","doi-asserted-by":"publisher","DOI":"10.1145\/3422622"},{"key":"ref133","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2020.06.005"},{"key":"ref134","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2019.00359"},{"key":"ref135","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2020.07.025"},{"key":"ref136","first-page":"1","article-title":"Multivariate time series imputation with generative adversarial networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Luo"},{"key":"ref137","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/429"},{"key":"ref138","first-page":"18","article-title":"NAOMI: Non-autoregressive multiresolution sequence imputation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Liu"},{"key":"ref139","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref140","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"ref141","first-page":"22419","article-title":"Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Wu"},{"key":"ref142","first-page":"1","article-title":"Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting","volume-title":"Proc. Int. Conf. Learn. Rep.","author":"Liu"},{"key":"ref143","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2022.3224880"},{"key":"ref144","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.119619"},{"key":"ref145","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.111270"},{"key":"ref146","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671751"},{"key":"ref147","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3615097"},{"key":"ref148","article-title":"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling","author":"Bai","year":"2018","journal-title":"arXiv:1803.01271"},{"key":"ref149","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2019.106973","article-title":"Learning representations of multivariate time series with missing data","volume":"96","author":"Bianchi","year":"2019","journal-title":"Pattern Recognit."},{"key":"ref150","article-title":"Time series representation models","author":"Leppich","year":"2024","journal-title":"arXiv:2405.18165"},{"key":"ref151","first-page":"5","article-title":"TimesNet: Temporal 2-D-variation modeling for general time series analysis","volume-title":"Proc. 11th Int. Conf. Learn. Rep.","author":"Wu"},{"key":"ref152","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671673"},{"key":"ref153","volume-title":"Improving language understanding by generative pre-training","author":"Radford","year":"2018"},{"issue":"8","key":"ref154","first-page":"9","article-title":"Language models are unsupervised multitask learners","volume":"1","author":"Radford","year":"2019","journal-title":"OpenAI Blog"},{"key":"ref155","first-page":"1877","article-title":"Language models are few-shot learners","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Brown"},{"key":"ref156","first-page":"2","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","volume-title":"Proc. naacL-HLT","author":"Kenton"},{"issue":"140","key":"ref157","first-page":"1","article-title":"Exploring the limits of transfer learning with a unified text-to-text transformer","volume":"21","author":"Raffel","year":"2020","journal-title":"J. Mach. Learn. Res."},{"key":"ref158","doi-asserted-by":"publisher","DOI":"10.1016\/j.eng.2019.12.014"},{"key":"ref159","first-page":"1","article-title":"Time-LLM: Time series forecasting by reprogramming large language models","volume-title":"Proc. 12th Int. Conf. Learn. Rep.","author":"Jin"},{"key":"ref160","first-page":"1","article-title":"S2IP-LLM: Semantic space informed prompt learning with LLM for time series forecasting","volume-title":"Proc. 40st Int. Conf. Mach. Learn.","author":"Pan"},{"key":"ref161","article-title":"Deep learning and LLM-based methods applied to stellar lightcurve classification","author":"Li","year":"2024","journal-title":"arXiv:2404.10757"},{"key":"ref162","first-page":"43322","article-title":"One fits all: Power general time series analysis by pretrained LM","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"36","author":"Zhou"},{"key":"ref163","article-title":"NuwaTS: Mending every incomplete time series","author":"Cheng","year":"2024","journal-title":"arXiv:2405.15317"},{"key":"ref164","first-page":"9","article-title":"TIMER: Generative pre-trained transformers are large time series models","volume-title":"Proc. 41st Int. Conf. Mach. Learn.","author":"Liu"},{"key":"ref165","first-page":"9","article-title":"MOMENT: A family of open time-series foundation models","volume-title":"Proc. 41st Int. Conf. Mach. Learn.","author":"Goswami"},{"key":"ref166","article-title":"CLAIM your data: Enhancing imputation accuracy with contextual large language models","author":"Hayat","year":"2024","journal-title":"arXiv:2405.17712"},{"key":"ref167","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2926321"},{"key":"ref168","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9206742"},{"key":"ref169","doi-asserted-by":"publisher","DOI":"10.1186\/s12874-024-02310-6"},{"key":"ref170","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2016.2530312"},{"key":"ref171","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2017.2771231"},{"key":"ref172","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2018.11.003"},{"key":"ref173","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2020.102622"},{"key":"ref174","doi-asserted-by":"publisher","DOI":"10.1016\/j.atmosenv.2004.02.026"},{"key":"ref175","doi-asserted-by":"publisher","DOI":"10.1016\/j.atmosenv.2018.11.053"},{"key":"ref176","doi-asserted-by":"publisher","DOI":"10.1016\/j.micpro.2020.103636"},{"key":"ref177","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2012.2236656"},{"key":"ref178","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2017.2730360"},{"key":"ref179","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.114743"},{"key":"ref180","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2021.3101831"},{"key":"ref181","doi-asserted-by":"publisher","DOI":"10.1161\/01.cir.101.23.e215"},{"key":"ref182","first-page":"3","article-title":"A public domain dataset for human activity recognition using smartphones","volume-title":"Proc. ESANN","volume":"3","author":"Anguita"},{"key":"ref183","doi-asserted-by":"publisher","DOI":"10.1109\/51.932724"},{"key":"ref184","volume-title":"PPG-BP database","author":"Liang","year":"2018"},{"key":"ref185","volume-title":"Daphnet freezing of gait","author":"Roggen","year":"2010"},{"key":"ref186","doi-asserted-by":"publisher","DOI":"10.3141\/1811-08"},{"key":"ref187","first-page":"6","article-title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting","volume-title":"Proc. Int. Conf. Learn. Rep. (ICLR)","author":"Li"},{"key":"ref188","doi-asserted-by":"publisher","DOI":"10.1109\/itsc.2018.8569552"},{"key":"ref189","volume-title":"OpenOASIS dataset","year":"2023"},{"issue":"2","key":"ref190","doi-asserted-by":"crossref","first-page":"750","DOI":"10.1016\/j.snb.2007.09.060","article-title":"On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario","volume":"129","author":"De Vito","year":"2008","journal-title":"Sensors Actuators B Chem"},{"key":"ref191","volume-title":"Electricity load diagrams 2011\u20132014","author":"Trindade","year":"2015"},{"key":"ref192","doi-asserted-by":"publisher","DOI":"10.5194\/essd-14-559-2022"},{"key":"ref193","volume-title":"Intel lab data","year":"2004"},{"key":"ref194","doi-asserted-by":"publisher","DOI":"10.1109\/TPAS.1979.319398"},{"key":"ref195","doi-asserted-by":"publisher","DOI":"10.1016\/j.jretconser.2010.03.011"},{"key":"ref196","doi-asserted-by":"publisher","DOI":"10.1016\/j.jretconser.2024.103993"},{"key":"ref197","volume-title":"Retail analysis with Walmart data","year":"2021"},{"key":"ref198","doi-asserted-by":"publisher","DOI":"10.1016\/j.jretai.2016.12.004"},{"key":"ref199","volume-title":"GPT-4 technical report","year":"2023"},{"key":"ref200","volume-title":"PaLM: Scaling language modeling with pathways","author":"Chowdhery et al","year":"2022"},{"key":"ref201","volume-title":"Claude: A safer AI assistant","year":"2023"},{"key":"ref202","volume-title":"LLM4TS: Aligning pre-trained LLMs as data-efficient time-series forecasters","author":"Chang","year":"2024"},{"key":"ref203","volume-title":"CALF: Aligning LLMs for time series forecasting via cross-modal fine-tuning","author":"Liu et al","year":"2024"},{"key":"ref204","article-title":"LoRA: Low-rank adaptation of large language models","author":"Hu","year":"2021","journal-title":"arXiv:2106.09685"},{"key":"ref205","doi-asserted-by":"publisher","DOI":"10.4135\/9781412952637.n77"},{"key":"ref206","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-023-10443-1"},{"key":"ref207","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120495"},{"key":"ref208","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.05.033"},{"key":"ref209","first-page":"32069","article-title":"Learning to reconstruct missing data from spatiotemporal graphs with sparse observations","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"35","author":"Marisca"},{"key":"ref210","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE55515.2023.00150"},{"key":"ref211","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"ref212","doi-asserted-by":"publisher","DOI":"10.58532\/nbennurtech2"},{"key":"ref213","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.03.091"},{"key":"ref214","first-page":"6696","article-title":"Neural controlled differential equations for irregular time series","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Kidger"},{"key":"ref215","doi-asserted-by":"publisher","DOI":"10.1007\/s10915-022-01939-z"},{"key":"ref216","article-title":"TEST: Text prototype aligned embedding to activate LLM\u2019s ability for time series","author":"Sun","year":"2023","journal-title":"arXiv:2308.08241"}],"container-title":["IEEE Communications Surveys &amp; Tutorials"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/9739\/11321210\/11071987.pdf?arnumber=11071987","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T18:18:21Z","timestamp":1767377901000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11071987\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":216,"URL":"https:\/\/doi.org\/10.1109\/comst.2025.3585962","relation":{},"ISSN":["1553-877X","2373-745X"],"issn-type":[{"value":"1553-877X","type":"electronic"},{"value":"2373-745X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}