{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T06:13:28Z","timestamp":1767766408042,"version":"3.48.0"},"reference-count":14,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T00:00:00Z","timestamp":1760832000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T00:00:00Z","timestamp":1760832000000},"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","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,19]]},"DOI":"10.1109\/vtc2025-fall65116.2025.11309967","type":"proceedings-article","created":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T18:33:45Z","timestamp":1767724425000},"page":"1-5","source":"Crossref","is-referenced-by-count":0,"title":["ELinear: An Efficient Linear Architecture for Edge Intelligence Time Series Forecasting"],"prefix":"10.1109","author":[{"given":"Chenyang","family":"He","sequence":"first","affiliation":[{"name":"Beijing Key Laboratory of Work Safety Intelligent Monitoring,Beijing,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zilong","family":"Yan","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Work Safety Intelligent Monitoring,Beijing,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianmu","family":"Sha","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Work Safety Intelligent Monitoring,Beijing,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Yao","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Work Safety Intelligent Monitoring,Beijing,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Work Safety Intelligent Monitoring,Beijing,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Work Safety Intelligent Monitoring,Beijing,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Work Safety Intelligent Monitoring,Beijing,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yinglei","family":"Teng","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Work Safety Intelligent Monitoring,Beijing,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Da","family":"Guo","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Work Safety Intelligent Monitoring,Beijing,China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2023.3275741"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3291371"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3484454"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2021.3075468"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i10.28991"},{"article-title":"Timesnet: Temporal 2d-variation modeling for general time series analysis","year":"2022","author":"Wu","key":"ref6"},{"article-title":"itransformer: Inverted transformers are effective for time series forecasting","volume-title":"The Twelfth International Conference on Learning Representations","author":"Liu","key":"ref7"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671928"},{"issue":"11","key":"ref9","first-page":"12608","article-title":"Hdmixer: Hierarchical dependency with extendable patch for multivariate time series forecasting","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"38","author":"Huang"},{"key":"ref10","first-page":"76656","article-title":"Frequency-domain mlps are more effective learners in time series forecasting","volume-title":"Advances in Neural Information Processing Systems","volume":"36","author":"Yi","year":"2023"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2024.109889"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2025.3525502"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2022.109384"},{"issue":"9","key":"ref14","first-page":"11121","article-title":"Are transformers effective for time series forecasting?","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"37","author":"Zeng"}],"event":{"name":"2025 IEEE 102nd Vehicular Technology Conference (VTC2025-Fall)","start":{"date-parts":[[2025,10,19]]},"location":"Chengdu, China","end":{"date-parts":[[2025,10,22]]}},"container-title":["2025 IEEE 102nd Vehicular Technology Conference (VTC2025-Fall)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11309821\/11309345\/11309967.pdf?arnumber=11309967","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T06:05:49Z","timestamp":1767765949000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11309967\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,19]]},"references-count":14,"URL":"https:\/\/doi.org\/10.1109\/vtc2025-fall65116.2025.11309967","relation":{},"subject":[],"published":{"date-parts":[[2025,10,19]]}}}