{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T18:32:24Z","timestamp":1783535544137,"version":"3.55.0"},"reference-count":60,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100004536","name":"Southwest Jiaotong University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004536","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFB3308500"],"award-info":[{"award-number":["2023YFB3308500"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1016\/j.neucom.2026.133022","type":"journal-article","created":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T16:10:25Z","timestamp":1770826225000},"page":"133022","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["Time-frequency-based pyramid channel network for long-term time series forecasting"],"prefix":"10.1016","volume":"676","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0360-4825","authenticated-orcid":false,"given":"Zhiqiang","family":"Jiang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongsheng","family":"Dong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-7634-032X","authenticated-orcid":false,"given":"Haotian","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaotong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"4","key":"10.1016\/j.neucom.2026.133022_bib0005","doi-asserted-by":"crossref","first-page":"2934","DOI":"10.1109\/TSG.2022.3224559","article-title":"A new framework for multivariate time series forecasting in energy management system","volume":"14","author":"Uremovi\u0107","year":"2023","journal-title":"IEEE Trans. Smart Grid"},{"issue":"4","key":"10.1016\/j.neucom.2026.133022_bib0010","doi-asserted-by":"crossref","first-page":"1979","DOI":"10.1109\/TSTE.2023.3268100","article-title":"Solar-mixer: an efficient end-to-end model for long-sequence photovoltaic power generation time series forecasting","volume":"14","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Sustain. Energy"},{"issue":"11","key":"10.1016\/j.neucom.2026.133022_bib0015","doi-asserted-by":"crossref","first-page":"2677","DOI":"10.1109\/TFUZZ.2020.2972823","article-title":"A fuzzy interval time-series energy and financial forecasting model using network-based multiple time-frequency spaces and the induced-ordered weighted averaging aggregation operation","volume":"28","author":"Liu","year":"2020","journal-title":"IEEE Trans. Fuzzy Syst."},{"issue":"1","key":"10.1016\/j.neucom.2026.133022_bib0020","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1109\/TNSM.2023.3295748","article-title":"Multivariate time series characterization and forecasting of voip traffic in real mobile networks","volume":"21","author":"Di Mauro","year":"2024","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"issue":"10","key":"10.1016\/j.neucom.2026.133022_bib0025","doi-asserted-by":"crossref","first-page":"3582","DOI":"10.1109\/TFUZZ.2023.3261893","article-title":"Nfig-x: nonlinear fuzzy information granule series for long-term traffic flow time-series forecasting","volume":"31","author":"Cheng","year":"2023","journal-title":"IEEE Trans. Fuzzy Syst."},{"issue":"4","key":"10.1016\/j.neucom.2026.133022_bib0030","doi-asserted-by":"crossref","first-page":"1281","DOI":"10.1109\/TFUZZ.2022.3198177","article-title":"Time-series forecasting based on fuzzy cognitive visibility graph and weighted multisubgraph similarity","volume":"31","author":"Hu","year":"2022","journal-title":"IEEE Trans. Fuzzy Syst."},{"issue":"9","key":"10.1016\/j.neucom.2026.133022_bib0035","doi-asserted-by":"crossref","first-page":"6227","DOI":"10.1109\/TNNLS.2021.3134792","article-title":"Gated spiking neural p systems for time series forecasting","volume":"34","author":"Liu","year":"2023","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"10","key":"10.1016\/j.neucom.2026.133022_bib0040","doi-asserted-by":"crossref","first-page":"10748","DOI":"10.1109\/TKDE.2023.3268199","article-title":"Multi-scale adaptive graph neural network for multivariate time series forecasting","volume":"35","author":"Chen","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.neucom.2026.133022_bib0045","first-page":"12677","article-title":"Film: frequency improved legendre memory model for long-term time series forecasting","volume":"35","author":"Zhou","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"1","key":"10.1016\/j.neucom.2026.133022_bib0050","doi-asserted-by":"crossref","first-page":"926","DOI":"10.1109\/TNSM.2021.3056399","article-title":"QOS time series modeling and forecasting for web services: a comprehensive survey","volume":"18","author":"Syu","year":"2021","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"issue":"1","key":"10.1016\/j.neucom.2026.133022_bib0055","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1109\/TFUZZ.2023.3298970","article-title":"Long-term time series forecasting with multilinear trend fuzzy information granules for LSTM in a periodic framework","volume":"32","author":"Zhu","year":"2024","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"10.1016\/j.neucom.2026.133022_bib0060","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"11106","article-title":"Informer: beyond efficient transformer for long sequence time-series forecasting","volume":"vol. 35","author":"Zhou","year":"2021"},{"key":"10.1016\/j.neucom.2026.133022_bib0065","author":"Lin"},{"issue":"11","key":"10.1016\/j.neucom.2026.133022_bib0070","doi-asserted-by":"crossref","first-page":"13586","DOI":"10.1109\/TPAMI.2023.3293516","article-title":"Difformer: multi-resolutional differencing transformer with dynamic ranging for time series analysis","volume":"45","author":"Li","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"3","key":"10.1016\/j.neucom.2026.133022_bib0075","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1109\/TFUZZ.2023.3309811","article-title":"Differential convolutional fuzzy time series forecasting","volume":"32","author":"Zhan","year":"2024","journal-title":"IEEE Trans. Fuzzy Syst."},{"issue":"8","key":"10.1016\/j.neucom.2026.133022_bib0080","first-page":"7665","article-title":"A multi-view multi-task learning framework for multi-variate time series forecasting","volume":"35","author":"Deng","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.neucom.2026.133022_bib0085","series-title":"The Eleventh International Conference on Learning Representations","article-title":"Micn: multi-scale local and global context modeling for long-term series forecasting","author":"Wang","year":"2023"},{"key":"10.1016\/j.neucom.2026.133022_bib0090","author":"Luo"},{"key":"10.1016\/j.neucom.2026.133022_bib0095","author":"Ye"},{"key":"10.1016\/j.neucom.2026.133022_bib0100","author":"Yuan"},{"key":"10.1016\/j.neucom.2026.133022_bib0105","series-title":"Proceedings of the 30th Annual International Conference on Mobile Computing and Networking","first-page":"77","article-title":"Rf-diffusion: radio signal generation via time-frequency diffusion","author":"Chi","year":"2024"},{"issue":"7","key":"10.1016\/j.neucom.2026.133022_bib0110","first-page":"6561","article-title":"A hybrid spiking neurons embedded LSTM network for multivariate time series learning under concept-drift environment","volume":"35","author":"Zheng","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"6","key":"10.1016\/j.neucom.2026.133022_bib0115","doi-asserted-by":"crossref","first-page":"3703","DOI":"10.1109\/TFUZZ.2024.3379853","article-title":"Multiple-input\u2013multiple-output randomized fuzzy cognitive map method for high-dimensional time series forecasting","volume":"32","author":"Orang","year":"2024","journal-title":"IEEE Trans. Fuzzy Syst."},{"issue":"2","key":"10.1016\/j.neucom.2026.133022_bib0120","doi-asserted-by":"crossref","first-page":"918","DOI":"10.1109\/TNSM.2023.3250512","article-title":"Spatial context-aware time-series forecasting for QOS prediction","volume":"20","author":"Zhou","year":"2023","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"issue":"6","key":"10.1016\/j.neucom.2026.133022_bib0125","doi-asserted-by":"crossref","first-page":"2504","DOI":"10.1109\/TKDE.2023.3323956","article-title":"Learning informative representation for fairness-aware multivariate time-series forecasting: a group-based perspective","volume":"36","author":"He","year":"2024","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.neucom.2026.133022_bib0130","series-title":"Advances in Neural Information Processing Systems","first-page":"22419","article-title":"Autoformer: decomposition transformers with auto-correlation for long-term series forecasting","volume":"vol. 34","author":"Wu","year":"2021"},{"key":"10.1016\/j.neucom.2026.133022_bib0135","article-title":"Timesnet: temporal 2d-variation modeling for general time series analysis","author":"Wu","year":"2023","journal-title":"11th Int. Conf. Learn. Represent."},{"key":"10.1016\/j.neucom.2026.133022_bib0140","first-page":"5816","article-title":"Scinet: time series modeling and forecasting with sample convolution and interaction","volume":"35","author":"Liu","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"2","key":"10.1016\/j.neucom.2026.133022_bib0145","first-page":"882","article-title":"Trustworthy uncertainty propagation for sequential time-series analysis in RNNS","volume":"36","author":"Dera","year":"2024","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.neucom.2026.133022_bib0150","series-title":"Advances in Neural Information Processing Systems","article-title":"Attention is all you need","volume":"vol. 30","author":"Vaswani","year":"2017"},{"key":"10.1016\/j.neucom.2026.133022_bib0155","series-title":"Proceedings of the 39th International Conference on Machine Learning","first-page":"27268","article-title":"FEDformer: frequency enhanced decomposed transformer for long-term series forecasting","author":"Zhou","year":"2022"},{"issue":"2","key":"10.1016\/j.neucom.2026.133022_bib0160","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1109\/TNNLS.2021.3100528","article-title":"Markovian RNN: an adaptive time series prediction network with hmm-based switching for nonstationary environments","volume":"34","author":"Ilhan","year":"2023","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"7","key":"10.1016\/j.neucom.2026.133022_bib0165","first-page":"7118","article-title":"Learning generative rnn-ode for collaborative time-series and event sequence forecasting","volume":"35","author":"Li","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"8","key":"10.1016\/j.neucom.2026.133022_bib0170","doi-asserted-by":"crossref","first-page":"8149","DOI":"10.1109\/TITS.2023.3266227","article-title":"Time series multi-step forecasting based on memory network for the prognostics and health management in freight train braking system","volume":"24","author":"Liu","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"11","key":"10.1016\/j.neucom.2026.133022_bib0175","doi-asserted-by":"crossref","first-page":"11067","DOI":"10.1109\/TKDE.2022.3231008","article-title":"An optimal hybrid bi-component series-parallel structure for time series forecasting","volume":"35","author":"Hajirahimi","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"9","key":"10.1016\/j.neucom.2026.133022_bib0180","doi-asserted-by":"crossref","first-page":"9168","DOI":"10.1109\/TKDE.2022.3221989","article-title":"Multivariate time series forecasting with dynamic graph neural ODES","volume":"35","author":"Jin","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.neucom.2026.133022_bib0185","series-title":"Proceedings of the 30th Annual International Conference on Mobile Computing and Networking","first-page":"77","article-title":"Rf-diffusion: radio signal generation via time-frequency diffusion","author":"Chi","year":"2024"},{"key":"10.1016\/j.neucom.2026.133022_bib0190","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"11121","article-title":"Are transformers effective for time series forecasting?","volume":"vol. 37","author":"Zeng","year":"2023"},{"issue":"2","key":"10.1016\/j.neucom.2026.133022_bib0195","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1109\/TCSVT.2020.2980876","article-title":"Joint anchor-feature refinement for real-time accurate object detection in images and videos","volume":"31","author":"Chen","year":"2021","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"6","key":"10.1016\/j.neucom.2026.133022_bib0200","doi-asserted-by":"crossref","first-page":"7213","DOI":"10.1007\/s40747-023-01102-7","article-title":"Multiple space based cascaded center point network for object detection","volume":"9","author":"Jiang","year":"2023","journal-title":"Complex Intell. Syst."},{"issue":"7","key":"10.1016\/j.neucom.2026.133022_bib0205","doi-asserted-by":"crossref","first-page":"4665","DOI":"10.1109\/TCYB.2021.3132704","article-title":"Training novel adaptive fuzzy cognitive map by knowledge-guidance learning mechanism for large-scale time-series forecasting","volume":"53","author":"Wang","year":"2023","journal-title":"IEEE Trans. Cybern."},{"issue":"1","key":"10.1016\/j.neucom.2026.133022_bib0210","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1109\/TPAMI.2022.3152862","article-title":"Deep time series forecasting with shape and temporal criteria","volume":"45","author":"Le Guen","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.neucom.2026.133022_bib0215","article-title":"Pytorch: an imperative style, high-performance deep learning library","volume":"32","author":"Paszke","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"3","key":"10.1016\/j.neucom.2026.133022_bib0220","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/MCAS.2016.2583681","article-title":"Recent developments in speech enhancement in the short-time Fourier transform domain","volume":"16","author":"Parchami","year":"2016","journal-title":"IEEE Circ. Syst. Mag."},{"issue":"2","key":"10.1016\/j.neucom.2026.133022_bib0225","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1109\/TASSP.1984.1164317","article-title":"Signal estimation from modified short-time Fourier transform","volume":"32","author":"Griffin","year":"1984","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"issue":"2","key":"10.1016\/j.neucom.2026.133022_bib0230","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1007\/s11668-023-01616-9","article-title":"An intelligent fault diagnosis method of rolling bearings based on short-time Fourier transform and convolutional neural network","volume":"23","author":"Zhang","year":"2023","journal-title":"J. Fail. Anal. Prev."},{"key":"10.1016\/j.neucom.2026.133022_bib0235","article-title":"A time series is worth 64 words: long-term forecasting with transformers","author":"Nie","year":"2023","journal-title":"11th Int. Conf. Learn. Represent, ICLR 2023"},{"key":"10.1016\/j.neucom.2026.133022_bib0240","series-title":"Advances in Neural Information Processing Systems","first-page":"76656","article-title":"Frequency-domain MLPS are more effective learners in time series forecasting","volume":"vol. 36","author":"Yi","year":"2023"},{"key":"10.1016\/j.neucom.2026.133022_bib0245","article-title":"Fits: modeling time series with 10k parameters","author":"Xu","year":"2024","journal-title":"12th Int. Conf. Learn. Represent, ICLR 2024"},{"key":"10.1016\/j.neucom.2026.133022_bib0250","article-title":"Itransformer: inverted transformers are effective for time series forecasting","author":"Liu","year":"2024","journal-title":"12th International Conference on Learning Representations, ICLR 2024"},{"key":"10.1016\/j.neucom.2026.133022_bib0255","article-title":"Crossformer: transformer utilizing cross-dimension dependency for multivariate time series forecasting","author":"Zhang","year":"2023","journal-title":"11th Int. Conf. Learn. Represent."},{"key":"10.1016\/j.neucom.2026.133022_bib0260","article-title":"Long-term forecasting with tide: time-series dense encoder","author":"Das","year":"2023","journal-title":"arXiv"},{"key":"10.1016\/j.neucom.2026.133022_bib0265","series-title":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD \u201924)","first-page":"446","article-title":"Drformer: multi-scale transformer utilizing diverse receptive fields for long time-series forecasting","author":"Ding","year":"2024"},{"key":"10.1016\/j.neucom.2026.133022_bib0270","author":"Kingma"},{"issue":"2","key":"10.1016\/j.neucom.2026.133022_bib0275","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1109\/TETC.2022.3230920","article-title":"Sepformer-based models: more efficient models for long sequence time-series forecasting","volume":"12","author":"Fan","year":"2024","journal-title":"IEEE Trans. Emerg. Top. Comput."},{"key":"10.1016\/j.neucom.2026.133022_bib0280","article-title":"Timekan: kan-based frequency decomposition learning architecture for long-term time series forecasting","author":"Huang","year":"2025","journal-title":"13th Int. Conf. Learn. Represent."},{"key":"10.1016\/j.neucom.2026.133022_bib0285","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1214\/aoms\/1177731944","article-title":"A comparison of alternative tests of significance for the problem of m rankings","volume":"11","author":"Friedman","year":"1940","journal-title":"Ann. Math. Stat."},{"issue":"12","key":"10.1016\/j.neucom.2026.133022_bib0290","doi-asserted-by":"crossref","first-page":"3890","DOI":"10.1016\/j.patcog.2014.06.002","article-title":"Incremental feature selection based on rough set in dynamic incomplete data","volume":"47","author":"Shu","year":"2014","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.neucom.2026.133022_bib0295","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.ijar.2022.11.020","article-title":"Semi-supervised feature selection for partially labeled mixed-type data based on multi-criteria measure approach","volume":"153","author":"Shu","year":"2023","journal-title":"Int. J. Approx. Reason."},{"issue":"5","key":"10.1016\/j.neucom.2026.133022_bib0300","doi-asserted-by":"crossref","first-page":"1377","DOI":"10.1109\/TETCI.2022.3231655","article-title":"Multi-label feature selection via label enhancement and analytic hierarchy process","volume":"7","author":"Huang","year":"2023","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226004194?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226004194?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T21:22:44Z","timestamp":1777584164000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231226004194"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":60,"alternative-id":["S0925231226004194"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133022","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Time-frequency-based pyramid channel network for long-term time series forecasting","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133022","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"133022"}}