{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T11:29:30Z","timestamp":1762514970104,"version":"build-2065373602"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2025,8,8]],"date-time":"2025-08-08T00:00:00Z","timestamp":1754611200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,8]],"date-time":"2025-08-08T00:00:00Z","timestamp":1754611200000},"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":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1007\/s13042-025-02778-8","type":"journal-article","created":{"date-parts":[[2025,8,8]],"date-time":"2025-08-08T10:18:27Z","timestamp":1754648307000},"page":"9685-9695","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GLinear: multivariate time series forecasting method towards controllable and efficient grouping"],"prefix":"10.1007","volume":"16","author":[{"given":"Gaoxiang","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Xiaoqiang","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,8]]},"reference":[{"key":"2778_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.resourpol.2022.102644","volume":"77","author":"L Zhang","year":"2022","unstructured":"Zhang L, Luo Q, Guo X, Umar M (2022) Medium-term and long-term volatility forecasts for eua futures with country-specific economic policy uncertainty indices. Resour Policy 77:102644","journal-title":"Resour Policy"},{"issue":"3","key":"2778_CR2","doi-asserted-by":"publisher","first-page":"1471","DOI":"10.1007\/s00704-019-02905-w","volume":"138","author":"P Aghelpour","year":"2019","unstructured":"Aghelpour P, Mohammadi B, Biazar SM (2019) Long-term monthly average temperature forecasting in some climate types of iran, using the models sarima, svr, and svr-fa. Theoret Appl Climatol 138(3):1471\u20131480","journal-title":"Theoret Appl Climatol"},{"doi-asserted-by":"crossref","unstructured":"Nooruldeen O, Alturki S, Baker MR, Ghareeb A (2022) Time series forecasting for decision making on city-wide energy demand: a comparative study. In: 2022 International Conference on Decision Aid Sciences and Applications (DASA), pp. 1706\u20131710 . IEEE","key":"2778_CR3","DOI":"10.1109\/DASA54658.2022.9765193"},{"unstructured":"Wang H, Peng J, Huang F, Wang J, Chen J, Xiao Y (2022) Micn: Multi-scale local and global context modeling for long-term series forecasting. In: The Eleventh International Conference on Learning Representations","key":"2778_CR4"},{"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: The Eleventh International Conference on Learning Representations","key":"2778_CR5"},{"doi-asserted-by":"crossref","unstructured":"Foumani SNM, Tan CW, Salehi M (2021) Disjoint-cnn for multivariate time series classification. In: 2021 International Conference on Data Mining Workshops (ICDMW), pp. 760\u2013769. IEEE","key":"2778_CR6","DOI":"10.1109\/ICDMW53433.2021.00099"},{"unstructured":"Nie Y, Nguyen NH, Sinthong P, Kalagnanam J (2023) A time series is worth 64 words: Long-term forecasting with transformers. In: The Eleventh International Conference on Learning Representations","key":"2778_CR7"},{"unstructured":"Zhang Y, Yan J (2022) Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting. In: The Eleventh International Conference on Learning Representations","key":"2778_CR8"},{"key":"2778_CR9","doi-asserted-by":"publisher","first-page":"11106","DOI":"10.1609\/aaai.v35i12.17325","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. Proceedings of the AAAI Conference on Artificial Intelligence 35:11106\u201311115","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"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":"2778_CR10"},{"key":"2778_CR11","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"},{"unstructured":"Liu S, Yu H, Liao C, Li J, Lin W, Liu AX, Dustdar S (2021) Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting. In: International Conference on Learning Representations","key":"2778_CR12"},{"unstructured":"Chen S-A, Li C-L, Arik SO, Yoder NC, Pfister T (2023) Tsmixer: An all-mlp architecture for time series forecast-ing. Transactions on Machine Learning Research","key":"2778_CR13"},{"key":"2778_CR14","doi-asserted-by":"publisher","first-page":"11121","DOI":"10.1609\/aaai.v37i9.26317","volume":"37","author":"A Zeng","year":"2023","unstructured":"Zeng A, Chen M, Zhang L, Xu Q (2023) Are transformers effective for time series forecasting? Proceedings of the AAAI Conference on Artificial Intelligence 37:11121\u201311128","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"unstructured":"Das A, Kong W, Leach A, Mathur SK, Sen R, Yu R (2023) Long-term forecasting with tide: Time-series dense encoder. Transactions on Machine Learning Research","key":"2778_CR15"},{"unstructured":"Li Z, Rao Z, Pan L, Xu Z (2023) Mts-mixers: Multivariate time series forecasting via factorized temporal and channel mixing. arXiv preprint arXiv:2302.04501","key":"2778_CR16"},{"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: The Twelfth International Conference on Learning Representations","key":"2778_CR17"},{"doi-asserted-by":"crossref","unstructured":"Chen J, Lenssen JE, Feng A, Hu W, Fey M, Tassiulas L, Leskovec J, Ying R (2024) From similarity to superiority: Channel clustering for time series forecasting. arXiv preprint arXiv:2404.01340","key":"2778_CR18","DOI":"10.52202\/079017-4152"},{"issue":"3","key":"2778_CR19","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.76.036106","volume":"76","author":"UN Raghavan","year":"2007","unstructured":"Raghavan UN, Albert R, Kumara S (2007) Near linear time algorithm to detect community structures in large-scale networks. Phys Rev E: Stat, Nonlin, Soft Matter Phys 76(3):036106","journal-title":"Phys Rev E: Stat, Nonlin, Soft Matter Phys"},{"doi-asserted-by":"crossref","unstructured":"Spearman C (1961) The proof and measurement of association between two things","key":"2778_CR20","DOI":"10.1037\/11491-005"},{"issue":"3","key":"2778_CR21","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","volume":"27","author":"CE Shannon","year":"1948","unstructured":"Shannon CE (1948) A mathematical theory of communication. The Bell system technical journal 27(3):379\u2013423","journal-title":"The Bell system technical journal"},{"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":"2778_CR22"},{"unstructured":"Kim T, Kim J, Tae Y, Park C, Choi J-H, Choo J (2021) Reversible instance normalization for accurate time-series forecasting against distribution shift. In: International Conference on Learning Representations","key":"2778_CR23"},{"issue":"10","key":"2778_CR24","doi-asserted-by":"publisher","first-page":"10008","DOI":"10.1088\/1742-5468\/2008\/10\/P10008","volume":"2008","author":"VD Blondel","year":"2008","unstructured":"Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech: Theory Exp 2008(10):10008","journal-title":"J Stat Mech: Theory Exp"},{"unstructured":"Lin S, Lin W, Wu W, Zhao F, Mo R, Zhang H (2023) Segrnn: Segment recurrent neural network for long-term time series forecasting. arXiv preprint arXiv:2308.11200","key":"2778_CR25"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02778-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-025-02778-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02778-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T11:24:51Z","timestamp":1762514691000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-025-02778-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,8]]},"references-count":25,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["2778"],"URL":"https:\/\/doi.org\/10.1007\/s13042-025-02778-8","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2025,8,8]]},"assertion":[{"value":"12 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 August 2025","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"}}]}}