{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T00:25:43Z","timestamp":1779236743786,"version":"3.51.4"},"reference-count":37,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"Guiyang University 2025 Research Production and Learning Special Project","award":["Gyu-yjs[2025]-22."],"award-info":[{"award-number":["Gyu-yjs[2025]-22."]}]},{"name":"Guiyang University New Degree Awarding Point Cultivation and Construction Project in 2025","award":["Gyxk202502"],"award-info":[{"award-number":["Gyxk202502"]}]},{"name":"Sixth Batch of Gui-Zhou Province High-level Innovative Talent Training Program","award":["Zhu Ke He Tong[2022]011"],"award-info":[{"award-number":["Zhu Ke He Tong[2022]011"]}]},{"name":"Research on Online Deep Transfer Learning of Time Series based on Financial Data","award":["Qian Ke He Ji Chu [2024] general 520"],"award-info":[{"award-number":["Qian Ke He Ji Chu [2024] general 520"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/access.2025.3645346","type":"journal-article","created":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T18:47:51Z","timestamp":1765997271000},"page":"214267-214279","source":"Crossref","is-referenced-by-count":1,"title":["DIMformer: A Dynamic Inverted Transformer With Mamba-Cross-Variable Linear Attention for Multivariate Time Series Forecasting"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-8128-6372","authenticated-orcid":false,"given":"Lin","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Electronic and Information Engineering, Guiyang University, Guiyang, Guizhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-6323-1718","authenticated-orcid":false,"given":"Hongfa","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Guiyang University, Guiyang, Guizhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1","article-title":"Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Zhang"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"ref3","first-page":"1","article-title":"Reformer: The efficient transformer","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Kitaev"},{"key":"ref4","first-page":"5243","article-title":"Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Li"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2019.07.001"},{"key":"ref6","first-page":"1","article-title":"N-BEATS: Neural basis expansion analysis for interpretable time series forecasting","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Oreshkin"},{"key":"ref7","first-page":"4837","article-title":"Think globally, act locally: A deep neural network approach to high-dimensional time series forecasting","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Sen"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210006"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref10","article-title":"TimeMixer: Decomposable multiscale mixing for time series forecasting","author":"Wang","year":"2024","journal-title":"arXiv:2405.14616"},{"key":"ref11","article-title":"ITransformer: Inverted transformers are effective for time series forecasting","author":"Liu","year":"2023","journal-title":"arXiv:2310.06625"},{"key":"ref12","first-page":"5156","article-title":"Transformers are RNNs: Fast autoregressive transformers with linear attention","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","volume":"1","author":"Katharopoulos"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1017\/9781108164818"},{"key":"ref14","volume-title":"Time Series Analysis, Forecasting and Control","author":"Box","year":"1976"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.3390\/fi15080255"},{"key":"ref16","first-page":"1","article-title":"MICN: Multiscale local and global context modeling for long-term series forecasting","volume-title":"Proc. 11th Int. Conf. Learn. Represent. (ICLR)","author":"Wang"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.113"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26317"},{"key":"ref19","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","author":"Devlin","year":"2018","journal-title":"arXiv:1810.04805"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref21","first-page":"22419","article-title":"Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Wu"},{"key":"ref22","first-page":"27268","article-title":"FEDformer: Frequency enhanced decomposed transformer for long-term series forecasting","volume-title":"Proc. 39th Int. Conf. Mach. Learn. (ICML)","volume":"162","author":"Zhou"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2025.3563070"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3480953"},{"key":"ref25","first-page":"9881","article-title":"Non-stationary transformers: Exploring the stationarity in time series forecasting","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Liu"},{"key":"ref26","article-title":"A time series is worth 64 words: Long-term forecasting with transformers","author":"Nie","year":"2022","journal-title":"arXiv:2211.14730"},{"key":"ref27","article-title":"TimeXer: Empowering transformers for time series forecasting with exogenous variables","author":"Wang","year":"2024","journal-title":"arXiv:2402.19072"},{"key":"ref28","article-title":"Demystify mamba in vision: A linear attention perspective","author":"Han","year":"2024","journal-title":"arXiv:2405.16605"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52734.2025.01388"},{"key":"ref30","article-title":"Mamba: Linear-time sequence modeling with selective state spaces","author":"Gu","year":"2023","journal-title":"arXiv:2312.00752"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.127063"},{"key":"ref32","first-page":"2488","article-title":"How does batch normalization help optimization?","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Santurkar"},{"key":"ref33","article-title":"Long-term forecasting with TiDE: Time-series dense encoder","author":"Das","year":"2023","journal-title":"arXiv:2304.08424"},{"key":"ref34","article-title":"TimesNet: Temporal 2D-variation modeling for general time series analysis","author":"Wu","year":"2022","journal-title":"arXiv:2210.02186"},{"key":"ref35","first-page":"8026","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Paszke"},{"key":"ref36","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014","journal-title":"arXiv:1412.6980"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1198\/073500102753410444"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10820123\/11303074.pdf?arnumber=11303074","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T05:34:31Z","timestamp":1766727271000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11303074\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":37,"URL":"https:\/\/doi.org\/10.1109\/access.2025.3645346","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}