{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T06:08:31Z","timestamp":1784095711090,"version":"3.55.0"},"reference-count":23,"publisher":"IEEE","license":[{"start":{"date-parts":[[2026,5,24]],"date-time":"2026-05-24T00:00:00Z","timestamp":1779580800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,5,24]],"date-time":"2026-05-24T00:00:00Z","timestamp":1779580800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,5,24]]},"DOI":"10.1109\/icc59461.2026.11586827","type":"proceedings-article","created":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T19:38:09Z","timestamp":1784057889000},"page":"1-6","source":"Crossref","is-referenced-by-count":0,"title":["LLM4Imp: Leveraging Frozen Large Language Models with Spectral Prompts for Time-Series Imputation"],"prefix":"10.1109","author":[{"given":"Franck Junior Aboya","family":"Messou","sequence":"first","affiliation":[{"name":"Hosei University,Graduate School of Science and Engineering,Tokyo,Japan,184-8584"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinhua","family":"Chen","sequence":"additional","affiliation":[{"name":"Hosei University,Graduate School of Science and Engineering,Tokyo,Japan,184-8584"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tong","family":"Liu","sequence":"additional","affiliation":[{"name":"Hosei University,Graduate School of Science and Engineering,Tokyo,Japan,184-8584"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shilong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Hosei University,Graduate School of Science and Engineering,Tokyo,Japan,184-8584"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiyu","family":"Wang","sequence":"additional","affiliation":[{"name":"Hosei University,Graduate School of Science and Engineering,Tokyo,Japan,184-8584"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Yu","sequence":"additional","affiliation":[{"name":"Hosei University,Graduate School of Science and Engineering,Tokyo,Japan,184-8584"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keping","family":"Yu","sequence":"additional","affiliation":[{"name":"Hosei University,Graduate School of Science and Engineering,Tokyo,Japan,184-8584"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-24271-9"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/VTC2024-Fall63153.2024.10757868"},{"key":"ref3","first-page":"6775","article-title":"Brits: Bidirectional recurrent imputation for time series","volume":"31","author":"Cao","year":"2018","journal-title":"Advances in Neural Information Processing Systems (NeurIPS)"},{"key":"ref4","first-page":"1651","article-title":"Gp-vae: Deep probabilistic time series imputation","volume-title":"Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS)","volume":"108","author":"Fortuin"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.119619"},{"key":"ref6","first-page":"5689","article-title":"Gain: Missing data imputation using generative adversarial nets","volume-title":"Proceedings of the 35th International Conference on Machine Learning (ICML)","volume":"80","author":"Yoon"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/429"},{"key":"ref8","article-title":"Filling the g_ap_s: Multivariate time series imputation by graph neural networks","volume-title":"International Conference on Learning Representations (ICLR)","author":"Cini"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICUFN65838.2025.11170021"},{"key":"ref10","article-title":"Reformer: The efficient transformer","volume-title":"International Conference on Learning Representations (ICLR)","author":"Kitaev"},{"key":"ref11","author":"Ding","year":"2023","journal-title":"Longnet: Scaling transformers to 1, 000, 000, 000 tokens"},{"key":"ref12","article-title":"Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting","volume-title":"International Conference on Learning Representations (ICLR)","author":"Zhang"},{"key":"ref13","article-title":"Tsmixer: An all-mlp architecture for time series forecasting","author":"Chen","year":"2023","journal-title":"Transactions on Machine Learning Research (TMLR)"},{"key":"ref14","article-title":"Timesnet: Temporal 2d-variation modeling for general time series analysis","volume-title":"International Conference on Learning Representations (ICLR)","author":"Wu"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2025.3596905"},{"key":"ref16","first-page":"12409","article-title":"Tslanet: Rethinking transformers for time series representation learning","volume-title":"Proceedings of the 41st International Conference on Machine Learning (ICML)","volume":"235","author":"Eldele"},{"key":"ref17","article-title":"Timemixer++: A general time series pattern machine for universal predictive analysis","volume-title":"International Conference on Learning Representations (ICLR)","author":"Wang"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1201\/9781003616719-7"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.52202\/075280-1877"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/icce67443.2026.11449688"},{"key":"ref21","article-title":"A time series is worth 64 words: Long-term forecasting with transformers","volume-title":"International Conference on Learning Representations (ICLR)","author":"Nie"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref23","author":"Du","year":"2023","journal-title":"PyPOTS: A python toolkit for machine learning on partially-observed time series"}],"event":{"name":"ICC 2026 - IEEE International Conference on Communications","location":"Glasgow, United Kingdom","start":{"date-parts":[[2026,5,24]]},"end":{"date-parts":[[2026,5,28]]}},"container-title":["ICC 2026 - IEEE International Conference on Communications"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11586754\/11586037\/11586827.pdf?arnumber=11586827","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T05:55:49Z","timestamp":1784094949000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11586827\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,24]]},"references-count":23,"URL":"https:\/\/doi.org\/10.1109\/icc59461.2026.11586827","relation":{},"subject":[],"published":{"date-parts":[[2026,5,24]]}}}