{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,7]],"date-time":"2026-06-07T08:22:36Z","timestamp":1780820556272,"version":"3.54.1"},"reference-count":51,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"12","license":[{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"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":["62450020"],"award-info":[{"award-number":["62450020"]}],"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":["62125306"],"award-info":[{"award-number":["62125306"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"State Key Laboratory of Industrial Control Technology, China","award":["ICT2025C01"],"award-info":[{"award-number":["ICT2025C01"]}]},{"name":"Open Research Project of the State Key Laboratory of Industrial Control Technology, China","award":["ICT2025B07"],"award-info":[{"award-number":["ICT2025B07"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Knowl. Data Eng."],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1109\/tkde.2025.3618763","type":"journal-article","created":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T17:52:18Z","timestamp":1759859538000},"page":"7001-7015","source":"Crossref","is-referenced-by-count":3,"title":["Breaking Information Granularity Heterogeneity: A Mutual Information-Inspired Causal Discovery Framework for Multi-Rate Time Series"],"prefix":"10.1109","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7374-8015","authenticated-orcid":false,"given":"Kun","family":"Zhu","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\/0000-0002-0254-5763","authenticated-orcid":false,"given":"Chunhui","family":"Zhao","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\/0000-0001-9082-2216","authenticated-orcid":false,"given":"Biao","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1038\/s41562-018-0466-5"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1126\/science.aay0214"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i15.29662"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3484461"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.2307\/1912791.1969"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jprocont.2022.06.011"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.52202\/079017-0569"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3065601"},{"key":"ref9","first-page":"1","article-title":"Interpretable models for granger causality using self-explaining neural networks","volume-title":"Proc. 9th Int. Conf. Learn. Representations","author":"Marcinkevi\u010ds"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i7.26031"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.3010022"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/asz007"},{"key":"ref13","first-page":"43423","article-title":"Causal discovery from subsampled time series with proxy variables","volume-title":"Proc. 37th Conf. Neural Inf. Process. Syst.","author":"Liu"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2021.3090996"},{"key":"ref15","first-page":"1898","article-title":"Discovering temporal causal relations from subsampled data","volume-title":"Proc. 32nd Int. Conf. Mach. Learn.","author":"Gong"},{"key":"ref16","article-title":"Causal discovery from temporally aggregated time series","volume-title":"Proc. Conf. Uncertainty Artif. Intell.","author":"Gong"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/9.24247"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1021\/ie801084e"},{"key":"ref19","first-page":"1","article-title":"Neural graphical modelling in continuous-time: Consistency guarantees and algorithms","volume-title":"Proc. 9th Int. Conf. Learn. Representations","author":"Bellot"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i10.29034"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2016.04.008"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2023.08.003"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3386984"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2025.3553957"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2023.3284397"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3119140"},{"key":"ref27","first-page":"1","article-title":"CUTS: Neural causal discovery from irregular time-series data","volume-title":"Proc. 11th Int. Conf. Learn. Representations","author":"Cheng"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3083401"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/34.917574"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2023.3247725"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482483"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/527"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107804"},{"key":"ref34","article-title":"Representation learning with contrastive predictive coding","author":"Oord","year":"2018"},{"key":"ref35","first-page":"15535","article-title":"Learning representations by maximizing mutual information across views","volume-title":"Proc. 33rd Conf. Neural Inf. Process. Syst.","author":"Bachman"},{"key":"ref36","first-page":"1","article-title":"Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization","volume-title":"Proc. 8th Int. Conf. Learn. Representations","author":"Sun"},{"key":"ref37","first-page":"237","article-title":"Fuzzy logic and probability","volume-title":"Proc. 11th Conf. Uncertainty Artif. Intell.","author":"H\u00e1jek"},{"key":"ref38","first-page":"1568","article-title":"Hypernetworks","volume-title":"Proc. 5th Int. Conf. Learn. Representations","author":"Ha"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2022.3215448"},{"key":"ref40","article-title":"MINE: Mutual information neural estimation","author":"Belghazi","year":"2018"},{"key":"ref41","first-page":"5606","article-title":"CLOCS: Contrastive learning of cardiac signals across space, time, and patients","volume-title":"Proc. 38th Int. Conf. Mach. Learn.","author":"Kiyasseh"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102811"},{"key":"ref43","first-page":"22419","article-title":"AutoFormer: Decomposition transformers with auto-correlation for long-term series forecasting","volume-title":"Proc. 35th Conf. Neural Inf. Process. Syst.","author":"Wu"},{"key":"ref44","first-page":"1","article-title":"CoST: Contrastive learning of disentangled seasonal-trend representations for time series forecasting","volume-title":"Proc. 9th Int. Conf. Learn. Representations","author":"Woo"},{"key":"ref45","first-page":"27268","article-title":"FEDformer: Frequency enhanced decomposed transformer for long-term series forecasting","volume-title":"Proc. 39th Int. Conf. Mach. Learn.","author":"Zhou"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2010.08.063"},{"issue":"11","key":"ref47","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.3390\/make1010019"},{"key":"ref49","first-page":"1942","article-title":"NTS-NOTEARS: Learning nonparametric DBNs with prior knowledge","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Sun"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.52202\/079017-3728"},{"key":"ref51","first-page":"1595","article-title":"DYNOTEARS: Structure learning from time-series data","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Pamfil"}],"container-title":["IEEE Transactions on Knowledge and Data Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/69\/11233837\/11195720.pdf?arnumber=11195720","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T19:47:03Z","timestamp":1762804023000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11195720\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12]]},"references-count":51,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.1109\/tkde.2025.3618763","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":[[2025,12]]}}}