{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T15:57:25Z","timestamp":1776095845059,"version":"3.50.1"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T00:00:00Z","timestamp":1776038400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T00:00:00Z","timestamp":1776038400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"DOI":"10.1007\/s11227-026-08470-0","type":"journal-article","created":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T15:10:49Z","timestamp":1776093049000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Adsnet: an adaptive dual-stream network for multivariate time series forecasting"],"prefix":"10.1007","volume":"82","author":[{"given":"Yifan","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junsan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,13]]},"reference":[{"issue":"2194","key":"8470_CR1","doi-asserted-by":"publisher","first-page":"20200209","DOI":"10.1098\/rsta.2020.0209","volume":"379","author":"B Lim","year":"2021","unstructured":"Lim B, Zohren S (2021) Time-series forecasting with deep learning: a survey. Phil Trans R Soc A 379(2194):20200209","journal-title":"Phil Trans R Soc A"},{"issue":"1","key":"8470_CR2","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1089\/big.2020.0159","volume":"9","author":"JF Torres","year":"2021","unstructured":"Torres JF, Hadjout D, Sebaa A, Mart\u00ednez-\u00c1lvarez F, Troncoso A (2021) Deep learning for time series forecasting: a survey. Big Data 9(1):3\u201321","journal-title":"Big Data"},{"key":"8470_CR3","doi-asserted-by":"publisher","first-page":"9881","DOI":"10.52202\/068431-0718","volume":"35","author":"Y Liu","year":"2022","unstructured":"Liu Y, Wu H, Wang J, Long M (2022) Non-stationary transformers: exploring the stationarity in time series forecasting. Adv Neural Inf Process Syst 35:9881\u20139893","journal-title":"Adv Neural Inf Process Syst"},{"key":"8470_CR4","unstructured":"Kim T, Kim J, Tae Y, Park C, Choi JH, Choo J (2021) Reversible instance normalization for accurate time-series forecasting against distribution shift. In: International Conference on Learning Representations"},{"key":"8470_CR5","first-page":"22419","volume":"34","author":"H Wu","year":"2021","unstructured":"Wu H, Xu J, Wang J, Long M (2021) Autoformer: decomposition transformers with auto-correlation for long-term series forecasting. Adv Neural Inf Process Syst 34:22419\u201322430","journal-title":"Adv Neural Inf Process Syst"},{"key":"8470_CR6","first-page":"11121","volume":"37","author":"A Zeng","year":"2023","unstructured":"Zeng A, Chen M, Zhang L, Xu Q (2023) Are transformers effective for time series forecasting? Proc AAAI Conf Artifi Intell 37:11121\u201311128","journal-title":"Proc AAAI Conf Artifi Intell"},{"key":"8470_CR7","unstructured":"Zhou T, Ma Z, Wen Q, Wang X, Sun L, Jin R (2022) Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In: International Conference on Machine Learning, pp. 27268\u201327286 . PMLR"},{"key":"8470_CR8","first-page":"63","volume":"30","author":"A Vaswani","year":"2017","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst 30:63","journal-title":"Adv Neural Inf Process Syst"},{"key":"8470_CR9","first-page":"11106","volume":"35","author":"H Zhou","year":"2021","unstructured":"Zhou H, Zhang S, Peng J, Zhang S, Li J, Xiong H, Zhang W (2021) Informer: beyond efficient transformer for long sequence time-series forecasting. Proc AAAI Conf Artifi Intell 35:11106\u201311115","journal-title":"Proc AAAI Conf Artifi Intell"},{"key":"8470_CR10","doi-asserted-by":"crossref","unstructured":"Wen Q, Zhou T, Zhang C, Chen W, Ma Z, Yan J, Sun L (2022) Transformers in time series: a survey. arXiv preprint arXiv:2202.07125","DOI":"10.24963\/ijcai.2023\/759"},{"key":"8470_CR11","unstructured":"Li Z, Qi S, Li Y, Xu Z (2023) Revisiting long-term time series forecasting: an investigation on linear mapping. arXiv preprint arXiv:2305.10721"},{"key":"8470_CR12","unstructured":"Xu Z, Zeng A, Xu Q (2023) Fits: modeling time series with $$10 k $$ parameters. arXiv preprint arXiv:2307.03756"},{"key":"8470_CR13","first-page":"20601","volume":"39","author":"A Stitsyuk","year":"2025","unstructured":"Stitsyuk A, Choi J (2025) xpatch: Dual-stream time series forecasting with exponential seasonal-trend decomposition. Proc AAAI Conf Artifi Intell 39:20601\u201320609","journal-title":"Proc AAAI Conf Artifi Intell"},{"key":"8470_CR14","unstructured":"Woo G, Liu C, Sahoo D, Kumar A, Hoi S (2022) Etsformer: exponential smoothing transformers for time-series forecasting. arXiv preprint arXiv:2202.01381"},{"key":"8470_CR15","unstructured":"Wang X, Zhou T, Wen Q, Gao J, Ding B, Jin R (2024) Card: Channel aligned robust blend transformer for time series forecasting. In: International Conference on Learning Representations"},{"key":"8470_CR16","unstructured":"Wu H, Hu T, Liu Y, Zhou H, Wang J, Long M (2023) Timesnet: temporal 2d-variation modeling for general time series analysis. In: International Conference on Learning Representations"},{"key":"8470_CR17","unstructured":"Wang H, Peng J, Huang F, Wang J, Chen J, Xiao Y (2023) Micn: multi-scale local and global context modeling for long-term series forecasting. In: The Eleventh International Conference on Learning Representations"},{"key":"8470_CR18","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, et al (2020) An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929"},{"key":"8470_CR19","unstructured":"Nie Y, Nguyen NH, Sinthong P, Kalagnanam J (2023) A time series is worth 64 words: long-term forecasting with transformers. In: International Conference on Learning Representations"},{"key":"8470_CR20","unstructured":"Goswami M, Szafer K, Choudhry A, Cai Y, Li S, Dubrawski A (2024) Moment: a family of open time-series foundation models. arXiv preprint arXiv:2402.03885"},{"key":"8470_CR21","unstructured":"Liu Y, Zhang H, Li C, Huang X, Wang J, Long M (2024) Timer: generative pre-trained transformers are large time series models. arXiv preprint arXiv:2402.02368"},{"key":"8470_CR22","unstructured":"Jin M, Wang S, Ma L, Chu Z, Zhang JY, Shi X, Chen PY, Liang Y, Li YF, Pan S, et al (2023) Time-llm: time series forecasting by reprogramming large language models. arXiv preprint arXiv:2310.01728"},{"key":"8470_CR23","doi-asserted-by":"crossref","unstructured":"Qiu X, Wu X, Lin Y, Guo C, Hu J, Yang B (2024) Duet: dual clustering enhanced multivariate time series forecasting. arXiv preprint arXiv:2412.10859","DOI":"10.1145\/3690624.3709325"},{"key":"8470_CR24","unstructured":"Liu Y, Hu T, Zhang H, Wu H, Wang S, Ma L, Long M (2024) itransformer: inverted transformers are effective for time series forecasting. In: International Conference on Learning Representations"},{"key":"8470_CR25","unstructured":"Zhang Y, Yan J (2023) Crossformer: transformer utilizing cross-dimension dependency for multivariate time series forecasting. In: The Eleventh International Conference on Learning Representations"},{"key":"8470_CR26","first-page":"65","volume":"32","author":"S Li","year":"2019","unstructured":"Li S, Jin X, Xuan Y, Zhou X, Chen W, Wang YX, Yan X (2019) Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting. Adv Neural Inf Process Syst 32:65","journal-title":"Adv Neural Inf Process Syst"},{"key":"8470_CR27","unstructured":"Ba JL, Kiros JR, Hinton GE (2016) Layer normalization. arXiv preprint arXiv:1607.06450"},{"key":"8470_CR28","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448\u2013456 . pmlr"},{"key":"8470_CR29","doi-asserted-by":"crossref","unstructured":"Chollet F (2017) Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251\u20131258","DOI":"10.1109\/CVPR.2017.195"},{"key":"8470_CR30","first-page":"21599","volume":"39","author":"Y Wu","year":"2025","unstructured":"Wu Y, Meng X, Hu H, Zhang J, Dong Y, Lu D (2025) Affirm: interactive mamba with adaptive fourier filters for long-term time series forecasting. Proc AAAI Conf Artifi Intell 39:21599\u201321607","journal-title":"Proc AAAI Conf Artifi Intell"},{"key":"8470_CR31","unstructured":"Wang S, Wu H, Shi X, Hu T, Luo H, Ma L, Zhang JY, Zhou J (2024) Timemixer: decomposable multiscale mixing for time series forecasting. arXiv preprint arXiv:2405.14616"},{"key":"8470_CR32","doi-asserted-by":"crossref","unstructured":"Qiu X, Hu J, Zhou L, Wu X, Du J, Zhang B, Guo C, Zhou A, Jensen CS, Sheng Z, et al (2024) Tfb: towards comprehensive and fair benchmarking of time series forecasting methods. arXiv preprint arXiv:2403.20150","DOI":"10.14778\/3665844.3665863"},{"key":"8470_CR33","first-page":"542","volume":"32","author":"A Paszke","year":"2019","unstructured":"Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L et al (2019) Pytorch: an imperative style, high-performance deep learning library. Adv Neural Inf Process Syst 32:542","journal-title":"Adv Neural Inf Process Syst"},{"key":"8470_CR34","unstructured":"Adam KDBJ, et al (2014) A method for stochastic optimization. arXiv preprint arXiv:1412.69801412(6) (2014)"},{"key":"8470_CR35","unstructured":"Hendrycks D, Gimpel K(2016) Gaussian error linear units (gelus). arXiv preprint arXiv:1606.08415"},{"issue":"1","key":"8470_CR36","first-page":"3","volume":"6","author":"RB Cleveland","year":"1990","unstructured":"Cleveland RB, Cleveland WS, McRae JE, Terpenning I et al (1990) Stl: a seasonal-trend decomposition. J off Stat 6(1):3\u201373","journal-title":"J off Stat"},{"key":"8470_CR37","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-71918-2","volume-title":"Forecasting with exponential smoothing: the state space approach","author":"R Hyndman","year":"2008","unstructured":"Hyndman R, Koehler A, Ord K, Snyder R (2008) Forecasting with exponential smoothing: the state space approach. Springer, Berlin"},{"issue":"6","key":"8470_CR38","doi-asserted-by":"publisher","first-page":"80","DOI":"10.2307\/3001968","volume":"1","author":"F Wilcoxon","year":"1945","unstructured":"Wilcoxon F (1945) Individual comparisons by ranking methods. Biomet Bull 1(6):80\u201383","journal-title":"Biomet Bull"},{"issue":"1","key":"8470_CR39","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1080\/00031305.2017.1380080","volume":"72","author":"SJ Taylor","year":"2018","unstructured":"Taylor SJ, Letham B (2018) Forecasting at scale. Am Stat 72(1):37\u201345","journal-title":"Am Stat"},{"issue":"1","key":"8470_CR40","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1086\/294743","volume":"38","author":"EF Fama","year":"1965","unstructured":"Fama EF (1965) The behavior of stock-market prices. J Bus 38(1):34\u2013105","journal-title":"J Bus"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-026-08470-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-026-08470-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-026-08470-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T15:11:06Z","timestamp":1776093066000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-026-08470-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,13]]},"references-count":40,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["8470"],"URL":"https:\/\/doi.org\/10.1007\/s11227-026-08470-0","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,13]]},"assertion":[{"value":"30 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 April 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"346"}}