{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T11:28:07Z","timestamp":1780054087675,"version":"3.54.0"},"reference-count":59,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"22","license":[{"start":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T00:00:00Z","timestamp":1731628800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T00:00:00Z","timestamp":1731628800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T00:00:00Z","timestamp":1731628800000},"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":["61836001"],"award-info":[{"award-number":["61836001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62102022"],"award-info":[{"award-number":["62102022"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Internet Things J."],"published-print":{"date-parts":[[2024,11,15]]},"DOI":"10.1109\/jiot.2024.3439672","type":"journal-article","created":{"date-parts":[[2024,8,9]],"date-time":"2024-08-09T17:27:53Z","timestamp":1723224473000},"page":"36979-36990","source":"Crossref","is-referenced-by-count":5,"title":["SGFM: Conditional Flow Matching for Time Series Anomaly Detection With State Space Models"],"prefix":"10.1109","volume":"11","author":[{"given":"Yongping","family":"He","sequence":"first","affiliation":[{"name":"School of Automation, Beijing Institute of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tijin","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Automation, Beijing Institute of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4528-1489","authenticated-orcid":false,"given":"Yufeng","family":"Zhan","sequence":"additional","affiliation":[{"name":"School of Automation, Beijing Institute of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zihang","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Automation, Beijing Institute of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5977-4911","authenticated-orcid":false,"given":"Yuanqing","family":"Xia","sequence":"additional","affiliation":[{"name":"School of Automation, Beijing Institute of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-71368-7_8"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3437880.3460413"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219845"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/1541880.1541882"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109743"},{"key":"ref6","first-page":"1","article-title":"Graph-augmented normalizing flows for anomaly detection of multiple time series","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Dai"},{"key":"ref7","first-page":"5508","article-title":"Time-series generative adversarial networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Yoon"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1213\/4\/042050"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/s11004-020-09892-z"},{"key":"ref10","first-page":"13016","article-title":"Timeseries anomaly detection using temporal hierarchical one-class network","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Shen"},{"key":"ref11","article-title":"LSTM-based encoder-decoder for multi-sensor anomaly detection","author":"Malhotra","year":"2016","journal-title":"arXiv:1607.00148"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.14778\/3514061.3514067"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403392"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-30490-4_56"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330672"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9053558"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2977892"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599391"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1002\/int.23027"},{"key":"ref21","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Ho"},{"key":"ref22","article-title":"Score-based generative modeling through stochastic differential equations","author":"Song","year":"2020","journal-title":"arXiv:2011.13456"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2958185"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3243391"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3011726"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/10.668741"},{"issue":"4","key":"ref27","first-page":"2","article-title":"Anomaly detection in time series of graphs using ARMA processes","volume":"24","author":"Pincombe","year":"2005","journal-title":"Asor Bull."},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ICCSN.2010.55"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.3390\/app11073194"},{"key":"ref30","first-page":"281","article-title":"Some methods for classification and analysis of multivariate observations","volume-title":"Proc. Berkeley Symp. Math. Statist. Probab.","volume":"1","author":"MacQueen"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.5555\/3001460.3001507"},{"key":"ref32","first-page":"1","article-title":"Deep autoencoding Gaussian mixture model for unsupervised anomaly detection","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Zong"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16523"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5758"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/264"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/505"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1609.02907"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1312.6114"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/3422622"},{"key":"ref40","first-page":"1","article-title":"Density estimation using real NVP","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Dinh"},{"key":"ref41","first-page":"1","article-title":"Denoising diffusion probabilistic models","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Ho"},{"key":"ref42","first-page":"1","article-title":"Graph attention networks","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Veli\u010dkovi\u0107"},{"key":"ref43","first-page":"1","article-title":"Flow matching for generative modeling","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Lipman"},{"key":"ref44","article-title":"Riemannian flow matching on general geometries","author":"Chen","year":"2023","journal-title":"arXiv:2302.03660"},{"key":"ref45","first-page":"1","article-title":"Flow matching for scalable simulation-based inference","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"36","author":"Wildberger"},{"key":"ref46","article-title":"Efficiently modeling long sequences with structured state spaces","author":"Gu","year":"2021","journal-title":"arXiv:2111.00396"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1207\/s15516709cog1402_1"},{"key":"ref48","first-page":"1474","article-title":"Hippo: Recurrent memory with optimal polynomial projections","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Gu"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref50","first-page":"1","article-title":"Neural ordinary differential equations","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Chen"},{"key":"ref51","first-page":"315","article-title":"Deep sparse rectifier neural networks","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Glorot"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.21437\/SSW.2016"},{"key":"ref53","article-title":"Guided image generation with conditional invertible neural networks","author":"Ardizzone","year":"2019","journal-title":"arXiv:1907.02392"},{"key":"ref54","article-title":"Density estimation using real NVP","author":"Dinh","year":"2016","journal-title":"arXiv:1605.08803"},{"key":"ref55","first-page":"1","article-title":"Masked autoregressive flow for density estimation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Papamakarios"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i7.20680"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3185996"},{"key":"ref58","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Glorot"},{"key":"ref59","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Finn"}],"container-title":["IEEE Internet of Things Journal"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6488907\/10747044\/10633235.pdf?arnumber=10633235","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:22:02Z","timestamp":1732666922000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10633235\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,15]]},"references-count":59,"journal-issue":{"issue":"22"},"URL":"https:\/\/doi.org\/10.1109\/jiot.2024.3439672","relation":{},"ISSN":["2327-4662","2372-2541"],"issn-type":[{"value":"2327-4662","type":"electronic"},{"value":"2372-2541","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,15]]}}}