{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T03:11:11Z","timestamp":1783825871049,"version":"3.55.0"},"reference-count":83,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"8","license":[{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"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":["62372430"],"award-info":[{"award-number":["62372430"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Postdoctoral Fellowship Program of CPSF","award":["GZC20241758"],"award-info":[{"award-number":["GZC20241758"]}]},{"DOI":"10.13039\/501100004739","name":"Youth Innovation Promotion Association of the Chinese Academy of Sciences","doi-asserted-by":"publisher","award":["2023112"],"award-info":[{"award-number":["2023112"]}],"id":[{"id":"10.13039\/501100004739","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Knowl. Data Eng."],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1109\/tkde.2025.3569649","type":"journal-article","created":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T13:48:13Z","timestamp":1747144093000},"page":"4635-4648","source":"Crossref","is-referenced-by-count":44,"title":["GinAR+: A Robust End-to-End Framework for Multivariate Time Series Forecasting With Missing Values"],"prefix":"10.1109","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8314-8251","authenticated-orcid":false,"given":"Chengqing","family":"Yu","sequence":"first","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3282-0535","authenticated-orcid":false,"given":"Fei","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0815-2768","authenticated-orcid":false,"given":"Zezhi","family":"Shao","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5694-3831","authenticated-orcid":false,"given":"Tangwen","family":"Qian","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6680-160X","authenticated-orcid":false,"given":"Zhao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4488-0102","authenticated-orcid":false,"given":"Wei","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7593-8293","authenticated-orcid":false,"given":"Zhulin","family":"An","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2749-2135","authenticated-orcid":false,"given":"Qi","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongjun","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3393996"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2024.124308"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.xinn.2023.100405"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.101819"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.xinn.2024.100691"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.14778\/3665844.3665863"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3417451"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102607"},{"key":"ref9","first-page":"17766","article-title":"Spectral temporal graph neural network for multivariate time-series forecasting","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Cao"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE60146.2024.00113"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3591106.3592306"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2022.103419"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3235312"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/3690624.3709325"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.xinn.2024.100763"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2025.103292"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3056502"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26350"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3218803"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/429"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583525"},{"key":"ref22","first-page":"17804","article-title":"Adaptive graph convolutional recurrent network for traffic forecasting","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Bai"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/505"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/264"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5477"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539396"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467330"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482000"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3240858"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/3589333"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1080\/13658816.2022.2032081"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2022.103820"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2910295"},{"key":"ref34","first-page":"1651","article-title":"GP-vae: Deep probabilistic time series imputation","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Fortuin"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/3557915.3560939"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3672055"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1016\/j.apr.2023.101717"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2022.126034"},{"key":"ref39","first-page":"1","article-title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Li"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3268199"},{"key":"ref41","first-page":"1","article-title":"Discrete graph structure learning for forecasting multiple time series","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Shang"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.14778\/3551793.3551827"},{"key":"ref43","article-title":"GatGPT: A pre-trained large language model with graph attention network for spatiotemporal imputation","author":"Chen","year":"2023"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16575"},{"key":"ref45","first-page":"32069","article-title":"Learning to reconstruct missing data from spatiotemporal graphs with sparse observations","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Marisca"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-99-1642-9_6"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.107217"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP49357.2023.10095054"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6056"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00929"},{"key":"ref51","first-page":"1","article-title":"Biased temporal convolution graph network for time series forecasting with missing values","volume-title":"Proc. 12th Int. Conf. Learn. Representations","author":"Chen"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3282989"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3371931"},{"key":"ref54","first-page":"30750","article-title":"Finding order in chaos: A novel data augmentation method for time series in contrastive learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Demirel"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599295"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3131584"},{"key":"ref57","first-page":"1","article-title":"Cost: Contrastive learning of disentangled seasonal-trend representations for time series forecasting","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Woo"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP48485.2024.10446875"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i4.25575"},{"key":"ref60","first-page":"22419","article-title":"Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Wu"},{"key":"ref61","first-page":"66687","article-title":"Parsimony or capability? Decomposition delivers both in long-term time series forecasting","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Deng"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557449"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2022.112405"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3615097"},{"key":"ref65","first-page":"1","article-title":"A time series is worth 64 words: Long-term forecasting with transformers","volume-title":"Proc. 11th Int. Conf. Learn. Representations","author":"Nie"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.119619"},{"key":"ref67","first-page":"1","article-title":"itransformer: Inverted transformers are effective for time series forecasting","volume-title":"Proc. 12th Int. Conf. Learn. Representations","author":"Liu"},{"key":"ref68","first-page":"24804","article-title":"CSDI: Conditional score-based diffusion models for probabilistic time series imputation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Tashiro"},{"key":"ref69","first-page":"43322","article-title":"One fits all: Power general time series analysis by pretrained LM","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zhou"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP48485.2024.10446144"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403118"},{"key":"ref72","first-page":"1","article-title":"Filling the g_ap_s: Multivariate time series imputation by graph neural networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Cini"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557702"},{"key":"ref74","first-page":"1","article-title":"Timesnet: Temporal 2D-variation modeling for general time series analysis","volume-title":"proc. 11th Int. Conf. Learn. Representations","author":"Wu"},{"key":"ref75","first-page":"69638","article-title":"Fouriergnn: Rethinking multivariate time series forecasting from a pure graph perspective","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Yi"},{"key":"ref76","article-title":"Deep time series models: A comprehensive survey and benchmark","author":"Wang","year":"2024"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3484454"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i7.25976"},{"key":"ref79","article-title":"Nuwats: Mending every incomplete time series","author":"Cheng","year":"2024"},{"key":"ref80","first-page":"29996","article-title":"SimMTM: A simple pre-training framework for masked time-series modeling","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Dong"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2020.117794"},{"key":"ref82","article-title":"An end-to-end time series model for simultaneous imputation and forecast","author":"Tran","year":"2023"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.202"}],"container-title":["IEEE Transactions on Knowledge and Data Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/69\/11072530\/11002729.pdf?arnumber=11002729","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,26]],"date-time":"2025-08-26T21:54:52Z","timestamp":1756245292000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11002729\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8]]},"references-count":83,"journal-issue":{"issue":"8"},"URL":"https:\/\/doi.org\/10.1109\/tkde.2025.3569649","relation":{},"ISSN":["1041-4347","1558-2191","2326-3865"],"issn-type":[{"value":"1041-4347","type":"print"},{"value":"1558-2191","type":"electronic"},{"value":"2326-3865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8]]}}}