{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T04:16:17Z","timestamp":1781842577530,"version":"3.54.5"},"reference-count":56,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key R&#x0026;D Program of China","award":["2021YFB2012300"],"award-info":[{"award-number":["2021YFB2012300"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62173101"],"award-info":[{"award-number":["62173101"]}],"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":["2022A1515011558"],"award-info":[{"award-number":["2022A1515011558"]}],"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":["2022A1515010865"],"award-info":[{"award-number":["2022A1515010865"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Laboratory of Guangdong Higher Education Institutes","award":["2023KSYS002"],"award-info":[{"award-number":["2023KSYS002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Knowl. Data Eng."],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1109\/tkde.2025.3650739","type":"journal-article","created":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T18:42:10Z","timestamp":1767638530000},"page":"1651-1664","source":"Crossref","is-referenced-by-count":2,"title":["DLTTS: Diffusion Model for Long-Tailed Time Series Generation in Industrial Scenarios"],"prefix":"10.1109","volume":"38","author":[{"given":"Weijun","family":"Hu","sequence":"first","affiliation":[{"name":"School of Electronics and Communication Engineering, Guangzhou University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haifeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"China Electronics Product Reliability and Environmental Test Institute, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenbin","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Electronics and Communication Engineering, Guangzhou University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaihong","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Electronics and Communication Engineering, Guangzhou University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinhao","family":"Long","sequence":"additional","affiliation":[{"name":"School of Electronics and Communication Engineering, Guangzhou University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6022-5381","authenticated-orcid":false,"given":"Wenli","family":"Shang","sequence":"additional","affiliation":[{"name":"School of Electronics and Communication Engineering, Guangzhou University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2301-8030","authenticated-orcid":false,"given":"Zhong","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Electronics and Communication Engineering, Guangzhou University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Rethinking class imbalance in machine learning","author":"Wu","year":"2023"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCC.2011.2161285"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2020.107175"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2018.02.016"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1631\/FITEE.2300310"},{"key":"ref6","article-title":"TimeVAE: A variational auto-encoder for multivariate time series generation","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Desai","year":"2022"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.03.120"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2023.06.033"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3310909"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2024.110190"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.jprocont.2024.103355"},{"key":"ref12","first-page":"5508","article-title":"Time-series generative adversarial networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Yoon","year":"2019"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ojies.2023.3319040"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/icopesa56898.2023.10140676"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/icpsasia58343.2023.10294871"},{"key":"ref16","first-page":"214","article-title":"Wasserstein generative adversarial networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Arjovsky","year":"2017"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2022.101762"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2024.120873"},{"key":"ref19","first-page":"1","article-title":"Long-tail learning via logit adjustment","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Menon","year":"2022"},{"key":"ref20","first-page":"4175","article-title":"Balanced meta-softmax for long-tailed visual recognition","volume-title":"Proc. Adv. Neural Inf. Process. Syst","author":"Ren","year":"2020"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr52688.2022.00777"},{"key":"ref22","first-page":"121699","article-title":"Utilizing image transforms and diffusion models for generative modeling of short and long time series","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"37","author":"Naiman","year":"2024"},{"key":"ref23","article-title":"Efficiently modeling long sequences with structured state spaces","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Gu","year":"2021"},{"key":"ref24","first-page":"11106","article-title":"Informer: Beyond efficient transformer for long sequence time-series forecasting","volume-title":"Proc. AAAI Conf. Artif. Intell.","author":"Zhou","year":"2020"},{"key":"ref25","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Ho","year":"2020"},{"key":"ref26","article-title":"Denoising diffusion implicit models","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Song","year":"2021"},{"key":"ref27","first-page":"8780","article-title":"Diffusion models beat GANs on image synthesis","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Dhariwal","year":"2021"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"ref29","article-title":"Prompt-to-prompt image editing with cross-attention control","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Hertz","year":"2023"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00387"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3361474"},{"key":"ref32","first-page":"8857","article-title":"Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Rasul","year":"2021"},{"key":"ref33","first-page":"28341","article-title":"Predict, refine, synthesize: Self-guiding diffusion models for probabilistic time series forecasting","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Kollovieh","year":"2023"},{"key":"ref34","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","year":"2021"},{"key":"ref35","article-title":"Diffusion-based time series imputation and forecasting with structured state apace models","author":"Alcaraz","year":"2023","journal-title":"Trans. Mach. Learn. Res."},{"key":"ref36","article-title":"Diffusion-TS: Interpretable diffusion for general time series generation","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Yuan","year":"2024"},{"key":"ref37","first-page":"23009","article-title":"Generative time series forecasting with diffusion, denoise, and disentanglement","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Li","year":"2022"},{"key":"ref38","article-title":"Regular time-series generation using SGM","author":"Lim","year":"2023"},{"key":"ref39","article-title":"DiffWave: A versatile diffusion model for audio synthesis","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kong","year":"2021"},{"key":"ref40","first-page":"61048","article-title":"On the constrained time-series generation problem","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Coletta","year":"2023"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2024.111481"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2024.3462500"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3509029"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2025.102965"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2023.3339245"},{"key":"ref46","article-title":"HyperTime: Implicit neural representation for time series","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Fons","year":"2022"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.3623086"},{"key":"ref48","first-page":"17564","article-title":"TabDDPM: Modelling tabular data with diffusion models","volume-title":"Proc. Int. Conf. Mach. Learn","author":"Akim","year":"2023"},{"key":"ref49","article-title":"TimeMixer++: A general time series pattern machine for universal predictive analysis","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Wang","year":"2025"},{"key":"ref50","article-title":"iTransformer: Inverted transformers are effective for time series forecasting","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Liu","year":"2024"},{"key":"ref51","article-title":"Long-term forecasting with TiDE: Time-series dense encoder","author":"Das","year":"2023","journal-title":"Transact. Mach. Learn. Res."},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26317"},{"key":"ref53","article-title":"Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Zhang","year":"2023"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/icassp49357.2023.10096881"},{"key":"ref55","article-title":"A time series is worth 64 words: Long-term forecasting with transformers","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Nie","year":"2023"},{"key":"ref56","article-title":"TSMixer: An All-MLP architecture for time series forecasting","author":"Chen","year":"2023","journal-title":"Transact. Mach. Learn. Res."}],"container-title":["IEEE Transactions on Knowledge and Data Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/69\/11393947\/11328910.pdf?arnumber=11328910","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T05:44:45Z","timestamp":1770961485000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11328910\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":56,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/tkde.2025.3650739","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":[[2026,3]]}}}