{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T18:30:38Z","timestamp":1771525838368,"version":"3.50.1"},"reference-count":38,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"10","license":[{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"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":["62173317"],"award-info":[{"award-number":["62173317"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Research and Development Program of Anhui","award":["202104a05020064"],"award-info":[{"award-number":["202104a05020064"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Artif. Intell."],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1109\/tai.2024.3410934","type":"journal-article","created":{"date-parts":[[2024,6,7]],"date-time":"2024-06-07T13:42:44Z","timestamp":1717767764000},"page":"5232-5243","source":"Crossref","is-referenced-by-count":3,"title":["Multivariate Time-Series Modeling and Forecasting With Parallelized Convolution and Decomposed Sparse-Transformer"],"prefix":"10.1109","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-6764-8649","authenticated-orcid":false,"given":"Shusen","family":"Ma","sequence":"first","affiliation":[{"name":"Institute of Advanced Technology, USTC, Shushan, Hefei, Anhui, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3684-5297","authenticated-orcid":false,"given":"Yun-Bo","family":"Zhao","sequence":"additional","affiliation":[{"name":"Institute of Advanced Technology and the Department of Automation, USTC, Shushan, Hefei, Anhui, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8706-3252","authenticated-orcid":false,"given":"Yu","family":"Kang","sequence":"additional","affiliation":[{"name":"Institute of Advanced Technology and the Department of Automation, USTC, Shushan, Hefei, Anhui, China"}]},{"given":"Peng","family":"Bai","sequence":"additional","affiliation":[{"name":"Department of Automation, USTC, Shushan, Hefei, Anhui, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2021.3123928"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSII.2022.3160266"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3276593"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2021.3128368"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2020.01.001"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-023-04980-z"},{"key":"ref7","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","year":"2021"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"ref9","article-title":"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling","author":"Bai","year":"2018"},{"key":"ref10","article-title":"Variational recurrent auto-encoders","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Fabius","year":"2015"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106042"},{"key":"ref12","article-title":"CoST: Contrastive learning of disentangled seasonal-trend representations for time series forecasting","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Woo","year":"2022"},{"key":"ref13","first-page":"38775","article-title":"Learning latent seasonal-trend representations for time series forecasting","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Wang","year":"2022"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20881"},{"key":"ref15","article-title":"Unsupervised representation learning for time series with temporal neighborhood coding","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Tonekaboni","year":"2021"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/icassp48485.2024.10446875"},{"key":"ref17","article-title":"Reformer: The efficient transformer","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kitaev","year":"2020"},{"key":"ref18","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","year":"2019"},{"key":"ref19","article-title":"Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Liu","year":"2022"},{"key":"ref20","first-page":"27268","article-title":"FEDformer: Frequency enhanced decomposed transformer for long-term series forecasting","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhou","year":"2022"},{"issue":"1","key":"ref21","first-page":"3","article-title":"STL: A seasonal-trend decomposition procedure based on loess","volume":"6","author":"Cleveland","year":"1990","journal-title":"J. Off. Statist."},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2021.3130529"},{"key":"ref23","first-page":"8700","article-title":"Decoupling local and global representations of time series","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","volume":"151","author":"Tonekaboni","year":"2022"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-08010-9_33"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.4324\/9781410605337-29"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-022-04100-3"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2023.108845"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2021.3063553"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.epsl.2022.117825"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109902"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2021.110861"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-018-9543-8"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.03.143"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2934110"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210006"},{"key":"ref37","first-page":"5816","article-title":"SCINet: Time series modeling and forecasting with sample convolution and interaction","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"35","author":"Liu","year":"2022"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/204"}],"container-title":["IEEE Transactions on Artificial Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/9078688\/10720652\/10552140.pdf?arnumber=10552140","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T01:09:25Z","timestamp":1755911365000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10552140\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10]]},"references-count":38,"journal-issue":{"issue":"10"},"URL":"https:\/\/doi.org\/10.1109\/tai.2024.3410934","relation":{},"ISSN":["2691-4581"],"issn-type":[{"value":"2691-4581","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10]]}}}