{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,5]],"date-time":"2026-07-05T05:14:46Z","timestamp":1783228486808,"version":"3.54.6"},"reference-count":53,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100012542","name":"Sichuan Province Science and Technology Support Program","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Knowledge-Based Systems"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.knosys.2026.116529","type":"journal-article","created":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T16:39:55Z","timestamp":1782491995000},"page":"116529","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["FreqResNet: Frequency-Aware Resonance Network for multivariate time series forecasting"],"prefix":"10.1016","volume":"349","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2860-4589","authenticated-orcid":false,"given":"Cheng","family":"Dai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-8137-5003","authenticated-orcid":false,"given":"Sha","family":"Xiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Banglie","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shoupeng","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lipeng","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.knosys.2026.116529_b1","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.113336","article-title":"Cross-city transfer learning for traffic forecasting via incremental distribution rectification","volume":"315","author":"Yang","year":"2025","journal-title":"Knowl.-Based Syst."},{"issue":"2","key":"10.1016\/j.knosys.2026.116529_b2","doi-asserted-by":"crossref","first-page":"1642","DOI":"10.1109\/JIOT.2022.3209523","article-title":"Adaptive spatiotemporal transformer graph network for traffic flow forecasting by IoT loop detectors","volume":"10","author":"Huang","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.knosys.2026.116529_b3","series-title":"Proceedings of the 2024 2nd Asia Conference on Computer Vision, Image Processing and Pattern Recognition","article-title":"Multivariate time series forecasting: A review","author":"Mendis","year":"2024"},{"key":"10.1016\/j.knosys.2026.116529_b4","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112951","article-title":"High\u2013low frequency dynamic interactive fusion network for multivariate time series forecasting","volume":"310","author":"Wang","year":"2025","journal-title":"Knowl.-Based Syst."},{"issue":"22","key":"10.1016\/j.knosys.2026.116529_b5","doi-asserted-by":"crossref","first-page":"22972","DOI":"10.1109\/JIOT.2022.3185010","article-title":"Coupledmuts: Coupled multivariate utility time-series representation and prediction","volume":"9","author":"Ren","year":"2022","journal-title":"IEEE Internet Things J."},{"issue":"10","key":"10.1016\/j.knosys.2026.116529_b6","doi-asserted-by":"crossref","first-page":"17790","DOI":"10.1109\/TITS.2025.3540519","article-title":"Clustered federated learning with adaptive pruning for 6g edge-intelligent transportation","volume":"26","author":"Lu","year":"2025","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.knosys.2026.116529_b7","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2023.111321","article-title":"Graphformer: Adaptive graph correlation transformer for multivariate long sequence time series forecasting","volume":"285","author":"Wang","year":"2024","journal-title":"Knowl.-Based Syst."},{"issue":"4","key":"10.1016\/j.knosys.2026.116529_b8","doi-asserted-by":"crossref","first-page":"3670","DOI":"10.1109\/JIOT.2024.3483038","article-title":"An improved reconstruction-based multiattribute contrastive learning for digital-twin-enabled industrial system","volume":"12","author":"Yang","year":"2025","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.knosys.2026.116529_b9","first-page":"469","article-title":"TimeXer: Empowering transformers for time series forecasting with exogenous variables","volume":"vol. 37","author":"Wang","year":"2024"},{"key":"10.1016\/j.knosys.2026.116529_b10","series-title":"Proceedings of the 41st International Conference on Machine Learning","article-title":"Sparsetsf: modeling long-term time series forecasting with 1k parameters","author":"Lin","year":"2024"},{"key":"10.1016\/j.knosys.2026.116529_b11","series-title":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","first-page":"459","article-title":"Tsmixer: Lightweight MLP-mixer model for multivariate time series forecasting","author":"Ekambaram","year":"2023"},{"key":"10.1016\/j.knosys.2026.116529_b12","series-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems","first-page":"6000","article-title":"Attention is all you need","author":"Vaswani","year":"2017"},{"key":"10.1016\/j.knosys.2026.116529_b13","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112556","article-title":"Periodformer: An efficient long-term time series forecasting method based on periodic attention","volume":"304","author":"Liang","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.knosys.2026.116529_b14","series-title":"TimeBridge: Non-stationarity matters for long-term time series forecasting","author":"Liu","year":"2025"},{"issue":"4","key":"10.1016\/j.knosys.2026.116529_b15","doi-asserted-by":"crossref","first-page":"3855","DOI":"10.1109\/TITS.2023.3234512","article-title":"Hierarchical spatio\u2013temporal graph convolutional networks and transformer network for traffic flow forecasting","volume":"24","author":"Huo","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.knosys.2026.116529_b16","first-page":"17766","article-title":"Spectral temporal graph neural network for multivariate time-series forecasting","volume":"vol. 33","author":"Cao","year":"2020"},{"key":"10.1016\/j.knosys.2026.116529_b17","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2022.108889","article-title":"TransLearn: A clustering based knowledge transfer strategy for improved time series forecasting","volume":"249","author":"Kohli","year":"2022","journal-title":"Knowl.-Based Syst."},{"issue":"12","key":"10.1016\/j.knosys.2026.116529_b18","doi-asserted-by":"crossref","first-page":"6750","DOI":"10.1109\/TKDE.2025.3619521","article-title":"A data-driven scale-adaptive time-frequency convolutional network for long sequence time-series forecasting","volume":"37","author":"Zhang","year":"2025","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.knosys.2026.116529_b19","series-title":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining","first-page":"6043","article-title":"Multi-modal time series analysis: A tutorial and survey","author":"Jiang","year":"2025"},{"key":"10.1016\/j.knosys.2026.116529_b20","series-title":"The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval","first-page":"95","article-title":"Modeling long- and short-term temporal patterns with deep neural networks","author":"Lai","year":"2018"},{"key":"10.1016\/j.knosys.2026.116529_b21","first-page":"11121","article-title":"Are transformers effective for time series forecasting?","volume":"vol. 37","author":"Zeng","year":"2023"},{"key":"10.1016\/j.knosys.2026.116529_b22","first-page":"38626","article-title":"TimeMixer: Decomposable multiscale mixing for time series forecasting","volume":"vol. 2024","author":"Wang","year":"2024"},{"key":"10.1016\/j.knosys.2026.116529_b23","first-page":"12640","article-title":"Unlocking the power of patch: Patch-based MLP for long-term time series forecasting","volume":"vol. 39","author":"Tang","year":"2025"},{"key":"10.1016\/j.knosys.2026.116529_b24","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.knosys.2026.116529_b25","series-title":"Proceedings of the Advances in Neural Information Processing Systems","article-title":"Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting","author":"Wu","year":"2021"},{"key":"10.1016\/j.knosys.2026.116529_b26","unstructured":"Y. Zhang, J. Yan, Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting, in: Proceedings of the Eleventh International Conference on Learning Representations, 2023."},{"key":"10.1016\/j.knosys.2026.116529_b27","series-title":"Proceedings of the Eleventh International Conference on Learning Representations","article-title":"A time series is worth 64 words: Long-term forecasting with transformers","author":"Nie","year":"2023"},{"key":"10.1016\/j.knosys.2026.116529_b28","series-title":"Proceedings of the Twelfth International Conference on Learning Representations","article-title":"Itransformer: Inverted transformers are effective for time series forecasting","author":"Liu","year":"2024"},{"key":"10.1016\/j.knosys.2026.116529_b29","series-title":"International Conference on Machine Learning","first-page":"8857","article-title":"Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting","author":"Rasul","year":"2021"},{"key":"10.1016\/j.knosys.2026.116529_b30","article-title":"Physical-guided temporal diffusion transformer for time series forecasting","volume":"303","author":"Yang","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.knosys.2026.116529_b31","series-title":"Proceedings of the 37th International Conference on Neural Information Processing Systems","article-title":"One fits all: power general time series analysis by pretrained LM","author":"Zhou","year":"2023"},{"key":"10.1016\/j.knosys.2026.116529_b32","series-title":"International Conference on Learning Representations","article-title":"Time-LLM: Time series forecasting by reprogramming large language models","author":"Jin","year":"2024"},{"key":"10.1016\/j.knosys.2026.116529_b33","series-title":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","first-page":"753","article-title":"Connecting the dots: Multivariate time series forecasting with graph neural networks","author":"Wu","year":"2020"},{"key":"10.1016\/j.knosys.2026.116529_b34","first-page":"46885","article-title":"CrossGNN: Confronting noisy multivariate time series via cross interaction refinement","volume":"vol. 36","author":"Huang","year":"2023"},{"issue":"4","key":"10.1016\/j.knosys.2026.116529_b35","doi-asserted-by":"crossref","first-page":"5496","DOI":"10.1109\/TCSS.2024.3372856","article-title":"Traffic origin-destination demand prediction via multichannel hypergraph convolutional networks","volume":"11","author":"Wang","year":"2024","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"issue":"4","key":"10.1016\/j.knosys.2026.116529_b36","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1109\/TBDATA.2024.3362188","article-title":"Dynamic hypergraph structure learning for multivariate time series forecasting","volume":"10","author":"Wang","year":"2024","journal-title":"IEEE Trans. Big Data"},{"key":"10.1016\/j.knosys.2026.116529_b37","article-title":"A self-attention enhanced hypergraph convolution network for traffic speed forecasting","volume":"647","author":"Wu","year":"2023","journal-title":"Inform. Sci."},{"issue":"12","key":"10.1016\/j.knosys.2026.116529_b38","doi-asserted-by":"crossref","first-page":"7891","DOI":"10.1109\/TITS.2021.3072743","article-title":"Metro passenger flow prediction via dynamic hypergraph convolution networks","volume":"22","author":"Wang","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.knosys.2026.116529_b39","series-title":"MSHyper: Multi-scale hypergraph transformer for long-term multivariate time series forecasting","author":"Shang","year":"2024"},{"key":"10.1016\/j.knosys.2026.116529_b40","first-page":"27268","article-title":"FEDformer: Frequency enhanced decomposed transformer for long-term series forecasting","volume":"vol. 162","author":"Zhou","year":"2022"},{"key":"10.1016\/j.knosys.2026.116529_b41","series-title":"Proceedings of the Eleventh International Conference on Learning Representations","article-title":"TimesNet: Temporal 2D-variation modeling for general time series analysis","author":"Wu","year":"2023"},{"key":"10.1016\/j.knosys.2026.116529_b42","first-page":"26295","article-title":"FITS: Modeling time series with 10k parameters","volume":"vol. 2024","author":"Xu","year":"2024"},{"key":"10.1016\/j.knosys.2026.116529_b43","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.knosys.2026.116529_b44","first-page":"69638","article-title":"Fouriergnn: Rethinking multivariate time series forecasting from a pure graph perspective","volume":"vol. 36","author":"Yi","year":"2023"},{"key":"10.1016\/j.knosys.2026.116529_b45","first-page":"11141","article-title":"MSGNet: Learning multi-scale inter-series correlations for multivariate time series forecasting","volume":"vol. 38","author":"Cai","year":"2024"},{"key":"10.1016\/j.knosys.2026.116529_b46","series-title":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","first-page":"3464","article-title":"Gcformer: An efficient solution for accurate and scalable long-term multivariate time series forecasting","author":"Zhao","year":"2023"},{"key":"10.1016\/j.knosys.2026.116529_b47","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.ins.2022.10.006","article-title":"Dynamic hypergraph neural networks based on key hyperedges","volume":"616","author":"Kang","year":"2022","journal-title":"Inform. Sci."},{"key":"10.1016\/j.knosys.2026.116529_b48","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1109\/LSP.2022.3148673","article-title":"RC filter design for wireless power transfer: A Fourier series approach","volume":"29","author":"Psomas","year":"2022","journal-title":"IEEE Signal Process. Lett."},{"issue":"7","key":"10.1016\/j.knosys.2026.116529_b49","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1109\/83.392336","article-title":"Optimal gabor filters for texture segmentation","volume":"4","author":"Dunn","year":"1995","journal-title":"IEEE Trans. Image Process."},{"issue":"1","key":"10.1016\/j.knosys.2026.116529_b50","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1109\/PROC.1978.10837","article-title":"On the use of windows for harmonic analysis with the discrete Fourier transform","volume":"66","author":"Harris","year":"1978","journal-title":"Proc. IEEE"},{"key":"10.1016\/j.knosys.2026.116529_b51","first-page":"64145","article-title":"SOFTS: Efficient multivariate time series forecasting with series-core fusion","volume":"vol. 37","author":"Han","year":"2024"},{"key":"10.1016\/j.knosys.2026.116529_b52","article-title":"Long-term forecasting with tiDE: Time-series dense encoder","author":"Das","year":"2023","journal-title":"Trans. Mach. Learn. Res."},{"key":"10.1016\/j.knosys.2026.116529_b53","unstructured":"Y. Li, et al., Transformer-modulated diffusion models for probabilistic multivariate time series forecasting, in: The Twelfth International Conference on Learning Representations, 2024."}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126012554?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126012554?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,5]],"date-time":"2026-07-05T04:14:57Z","timestamp":1783224897000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0950705126012554"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":53,"alternative-id":["S0950705126012554"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116529","relation":{},"ISSN":["0950-7051"],"issn-type":[{"value":"0950-7051","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"FreqResNet: Frequency-Aware Resonance Network for multivariate time series forecasting","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116529","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":"116529"}}