{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T00:58:33Z","timestamp":1781225913117,"version":"3.54.1"},"reference-count":69,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","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":[[2026,6]]},"DOI":"10.1016\/j.knosys.2026.116022","type":"journal-article","created":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T15:02:24Z","timestamp":1776697344000},"page":"116022","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["DTFNet: A dual-modal time\u2013frequency fusion network for non-stationary time series modeling"],"prefix":"10.1016","volume":"343","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-9499-1491","authenticated-orcid":false,"given":"Caixia","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0343-3499","authenticated-orcid":false,"given":"Fan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8978-1058","authenticated-orcid":false,"given":"Xiaofeng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8844-9667","authenticated-orcid":false,"given":"Hua","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.knosys.2026.116022_b1","series-title":"Are transformers effective for time series forecasting?","first-page":"11121","author":"Zeng","year":"2023"},{"key":"10.1016\/j.knosys.2026.116022_b2","doi-asserted-by":"crossref","unstructured":"Z. Wu, S. Pan, G. Long, J. Jiang, X. Chang, C. Zhang, Connecting the dots: Multivariate time series forecasting with graph neural networks, in: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020, pp. 753\u2013763.","DOI":"10.1145\/3394486.3403118"},{"key":"10.1016\/j.knosys.2026.116022_b3","unstructured":"H. Wu, T. Hu, Y. Liu, H. Zhou, J. Wang, M. Long, TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis, in: The Eleventh International Conference on Learning Representations."},{"issue":"3","key":"10.1016\/j.knosys.2026.116022_b4","doi-asserted-by":"crossref","first-page":"325","DOI":"10.2307\/1191800","article-title":"Academic tenure and academic freedom","volume":"53","author":"Brown","year":"1990","journal-title":"Law Contemp. Probl."},{"key":"10.1016\/j.knosys.2026.116022_b5","unstructured":"Y. Zhang, J. Yan, Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting, in: The Eleventh International Conference on Learning Representations, 2023."},{"key":"10.1016\/j.knosys.2026.116022_b6","first-page":"17766","article-title":"Spectral temporal graph neural network for multivariate time-series forecasting","volume":"33","author":"Cao","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.116022_b7","first-page":"11106","article-title":"Informer: Beyond efficient transformer for long sequence time-series forecasting","volume":"vol. 35, no. 12","author":"Zhou","year":"2021"},{"key":"10.1016\/j.knosys.2026.116022_b8","first-page":"22419","article-title":"Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting","volume":"34","author":"Wu","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.116022_b9","unstructured":"S.A. Chen, C.L. Li, S.O. Arik, N.C. Yoder, T. Pfister, TSMixer: An All-MLP Architecture for Time Series Forecast-ing, Trans. Mach. Learn. Res.."},{"key":"10.1016\/j.knosys.2026.116022_b10","series-title":"International Conference on Machine Learning","first-page":"27268","article-title":"Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting","author":"Zhou","year":"2022"},{"key":"10.1016\/j.knosys.2026.116022_b11","unstructured":"S. Liu, H. Yu, C. Liao, J. Li, W. Lin, A.X. Liu, S. Dustdar, Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting, in: International Conference on Learning Representations."},{"key":"10.1016\/j.knosys.2026.116022_b12","doi-asserted-by":"crossref","unstructured":"K. Yi, Q. Zhang, W. Fan, L. Cao, S. Wang, H. He, G. Long, L. Hu, Q. Wen, H. Xiong, A survey on deep learning based time series analysis with frequency transformation, in: Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 2, 2025, pp. 6206\u20136215.","DOI":"10.1145\/3711896.3736571"},{"key":"10.1016\/j.knosys.2026.116022_b13","first-page":"76656","article-title":"Frequency-domain MLPs are more effective learners in time series forecasting","volume":"36","author":"Yi","year":"2023","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.116022_b14","first-page":"55115","article-title":"Filternet: Harnessing frequency filters for time series forecasting","volume":"37","author":"Yi","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.116022_b15","first-page":"70033","article-title":"Frequency domain-based dataset distillation","volume":"36","author":"Shin","year":"2023","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.116022_b16","article-title":"MCNR: Multiscale feature-based latent data component extraction linear regression model","author":"Lu","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.knosys.2026.116022_b17","doi-asserted-by":"crossref","unstructured":"S. Yao, A. Piao, W. Jiang, Y. Zhao, H. Shao, S. Liu, D. Liu, J. Li, T. Wang, S. Hu, et al., Stfnets: Learning sensing signals from the time-frequency perspective with short-time fourier neural networks, in: The World Wide Web Conference, 2019, pp. 2192\u20132202.","DOI":"10.1145\/3308558.3313426"},{"issue":"7","key":"10.1016\/j.knosys.2026.116022_b18","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1109\/34.192463","article-title":"A theory for multiresolution signal decomposition: the wavelet representation","volume":"11","author":"Mallat","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"432","key":"10.1016\/j.knosys.2026.116022_b19","doi-asserted-by":"crossref","first-page":"1200","DOI":"10.1080\/01621459.1995.10476626","article-title":"Adapting to unknown smoothness via wavelet shrinkage","volume":"90","author":"Donoho","year":"1995","journal-title":"J. Amer. Statist. Assoc."},{"key":"10.1016\/j.knosys.2026.116022_b20","series-title":"Wavelet Methods for Time Series Analysis","author":"Percival","year":"2000"},{"key":"10.1016\/j.knosys.2026.116022_b21","unstructured":"A. van den Oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals, A. Graves, N. Kalchbrenner, A. Senior, K. Kavukcuoglu, WaveNet: A Generative Model for Raw Audio, in: Proc. SSW 2016, 2016, pp. 125\u2013125."},{"key":"10.1016\/j.knosys.2026.116022_b22","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."},{"key":"10.1016\/j.knosys.2026.116022_b23","unstructured":"M.X.B. Rodriguez, A. Gruson, L. Polania, S. Fujieda, F. Prieto, K. Takayama, T. Hachisuka, Deep adaptive wavelet network, in: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, 2020, pp. 3111\u20133119."},{"key":"10.1016\/j.knosys.2026.116022_b24","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2023.102158","article-title":"FECAM: Frequency enhanced channel attention mechanism for time series forecasting","volume":"58","author":"Jiang","year":"2023","journal-title":"Adv. Eng. Inform."},{"key":"10.1016\/j.knosys.2026.116022_b25","article-title":"WaveRoRA: Wavelet rotary route attention for multivariate time series forecasting","author":"Liang","year":"2025","journal-title":"IEEE Trans. Mob. Comput."},{"key":"10.1016\/j.knosys.2026.116022_b26","unstructured":"X. Piao, Z. Chen, T. Murayama, Y. Matsubara, Y. Sakurai, Divide-and-conquer time series forecasting with auto-frequency-correlation via cross-channel attention."},{"key":"10.1016\/j.knosys.2026.116022_b27","series-title":"FreEformer: Frequency enhanced transformer for multivariate time series forecasting","author":"Yue","year":"2025"},{"key":"10.1016\/j.knosys.2026.116022_b28","unstructured":"T. Dai, B. Wu, P. Liu, N. Li, J. Bao, Y. Jiang, S.T. Xia, Periodicity decoupling framework for long-term series forecasting, in: The Twelfth International Conference on Learning Representations, 2024."},{"key":"10.1016\/j.knosys.2026.116022_b29","doi-asserted-by":"crossref","DOI":"10.1109\/TKDE.2025.3556940","article-title":"Inconsistent multivariate time series forecasting","author":"Shen","year":"2025","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.knosys.2026.116022_b30","series-title":"Time Series Analysis: Forecasting and Control","author":"Box","year":"2015"},{"issue":"8","key":"10.1016\/j.knosys.2026.116022_b31","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"10.1016\/j.knosys.2026.116022_b32","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"3","key":"10.1016\/j.knosys.2026.116022_b33","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1007\/s41095-023-0369-x","article-title":"CF-DAN: Facial-expression recognition based on cross-fusion dual-attention network","volume":"10","author":"Zhang","year":"2024","journal-title":"Comput. Vis. Media"},{"key":"10.1016\/j.knosys.2026.116022_b34","unstructured":"W. Zhang, H. Wang, F. Zhang, Skip-timeformer: Skip-time interaction transformer for long sequence time-series forecasting, in: International Joint Conference on Artificial Intelligence, 2024, pp. 5499\u20135507."},{"key":"10.1016\/j.knosys.2026.116022_b35","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2024.121659","article-title":"THATSN: Temporal hierarchical aggregation tree structure network for long-term time-series forecasting","volume":"692","author":"Zhang","year":"2025","journal-title":"Inform. Sci."},{"key":"10.1016\/j.knosys.2026.116022_b36","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.111013","article-title":"Probabilistic intervals prediction based on adaptive regression with attention residual connections and covariance constraints","volume":"156","author":"Zhang","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.knosys.2026.116022_b37","article-title":"Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting","volume":"32","author":"Li","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"7","key":"10.1016\/j.knosys.2026.116022_b38","first-page":"1","article-title":"TFP-mixer: A lightweight time and frequency combining model for multivariate long-term time series forecasting","volume":"55","author":"Zhang","year":"2025","journal-title":"Appl. Intell."},{"key":"10.1016\/j.knosys.2026.116022_b39","series-title":"Breakthroughs in Statistics: Methodology and Distribution","first-page":"492","article-title":"Robust estimation of a location parameter","author":"Huber","year":"1992"},{"issue":"4","key":"10.1016\/j.knosys.2026.116022_b40","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1257\/jep.15.4.143","article-title":"Quantile regression","volume":"15","author":"Koenker","year":"2001","journal-title":"J. Econ. Perspect."},{"issue":"529","key":"10.1016\/j.knosys.2026.116022_b41","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1080\/01621459.2018.1543124","article-title":"Adaptive huber regression","volume":"115","author":"Sun","year":"2020","journal-title":"J. Amer. Statist. Assoc."},{"key":"10.1016\/j.knosys.2026.116022_b42","doi-asserted-by":"crossref","unstructured":"J.T. Barron, A general and adaptive robust loss function, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 4331\u20134339.","DOI":"10.1109\/CVPR.2019.00446"},{"issue":"11","key":"10.1016\/j.knosys.2026.116022_b43","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1016\/j.tics.2010.09.001","article-title":"The functional role of cross-frequency coupling","volume":"14","author":"Canolty","year":"2010","journal-title":"Trends Cogn. Sci."},{"key":"10.1016\/j.knosys.2026.116022_b44","unstructured":"M. Priestley, Spectral Analysis and Time Series 1981 London, New York Academic Press."},{"key":"10.1016\/j.knosys.2026.116022_b45","series-title":"A Wavelet Tour of Signal Processing","author":"Mallat","year":"1999"},{"issue":"1","key":"10.1016\/j.knosys.2026.116022_b46","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1175\/1520-0477(1998)079<0061:APGTWA>2.0.CO;2","article-title":"A practical guide to wavelet analysis","volume":"79","author":"Torrence","year":"1998","journal-title":"Bull. Am. Meteorol. Soc."},{"issue":"5\/6","key":"10.1016\/j.knosys.2026.116022_b47","doi-asserted-by":"crossref","first-page":"561","DOI":"10.5194\/npg-11-561-2004","article-title":"Application of the cross wavelet transform and wavelet coherence to geophysical time series","volume":"11","author":"Grinsted","year":"2004","journal-title":"Nonlinear Process. Geophys."},{"key":"10.1016\/j.knosys.2026.116022_b48","series-title":"Natural Image Statistics: A Probabilistic Approach to Early Computational Vision","first-page":"151","article-title":"Independent component analysis","author":"Hyv\u00e4rinen","year":"2001"},{"key":"10.1016\/j.knosys.2026.116022_b49","doi-asserted-by":"crossref","unstructured":"Y. Chen, X. Dai, M. Liu, D. Chen, L. Yuan, Z. Liu, Dynamic convolution: Attention over convolution kernels, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 11030\u201311039.","DOI":"10.1109\/CVPR42600.2020.01104"},{"key":"10.1016\/j.knosys.2026.116022_b50","doi-asserted-by":"crossref","unstructured":"J. Hu, L. Shen, G. Sun, Squeeze-and-excitation networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 7132\u20137141.","DOI":"10.1109\/CVPR.2018.00745"},{"issue":"6583","key":"10.1016\/j.knosys.2026.116022_b51","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1038\/381607a0","article-title":"Emergence of simple-cell receptive field properties by learning a sparse code for natural images","volume":"381","author":"Olshausen","year":"1996","journal-title":"Nature"},{"key":"10.1016\/j.knosys.2026.116022_b52","doi-asserted-by":"crossref","first-page":"9881","DOI":"10.52202\/068431-0718","article-title":"Non-stationary transformers: Exploring the stationarity in time series forecasting","volume":"35","author":"Liu","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.116022_b53","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2024.120626","article-title":"HFN: Heterogeneous feature network for multivariate time series anomaly detection","volume":"670","author":"Zhan","year":"2024","journal-title":"Inform. Sci."},{"key":"10.1016\/j.knosys.2026.116022_b54","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.111412","article-title":"TFDNet: Time\u2013frequency enhanced decomposed network for long-term time series forecasting","volume":"162","author":"Luo","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.knosys.2026.116022_b55","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.128960","article-title":"TCM: An efficient lightweight MLP-based network with affine transformation for long-term time series forecasting","volume":"617","author":"Jiang","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.knosys.2026.116022_b56","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"19581","article-title":"Wpmixer: Efficient multi-resolution mixing for long-term time series forecasting","author":"Murad","year":"2025"},{"key":"10.1016\/j.knosys.2026.116022_b57","doi-asserted-by":"crossref","unstructured":"Z. Liu, J. Yang, M. Cheng, Y. Luo, Z. Li, Generative pretrained hierarchical transformer for time series forecasting, in: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024, pp. 2003\u20132013.","DOI":"10.1145\/3637528.3671855"},{"key":"10.1016\/j.knosys.2026.116022_b58","first-page":"64145","article-title":"Softs: Efficient multivariate time series forecasting with series-core fusion","volume":"37","author":"Han","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.116022_b59","unstructured":"H. Chen, V. Luong, L. Mukherjee, V. Singh, SimpleTM: A Simple Baseline for Multivariate Time Series Forecasting, in: The Thirteenth International Conference on Learning Representations, 2025."},{"key":"10.1016\/j.knosys.2026.116022_b60","unstructured":"X. Ma, Z.L. Ni, S. Xiao, X. Chen, TimePro: Efficient Multivariate Long-term Time Series Forecasting with Variable-and Time-Aware Hyper-state, in: Forty-Second International Conference on Machine Learning."},{"issue":"5","key":"10.1016\/j.knosys.2026.116022_b61","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1007\/s10994-025-06758-5","article-title":"CPAT: cross-patch aggregated transformer for time series forecasting","volume":"114","author":"Liu","year":"2025","journal-title":"Mach. Learn."},{"key":"10.1016\/j.knosys.2026.116022_b62","unstructured":"Y. Hu, G. Zhang, P. Liu, D. Lan, N. Li, D. Cheng, T. Dai, S.T. Xia, S. Pan, TimeFilter: Patch-Specific Spatial-Temporal Graph Filtration for Time Series Forecasting, in: Forty-Second International Conference on Machine Learning."},{"key":"10.1016\/j.knosys.2026.116022_b63","unstructured":"S. Huang, Z. Zhao, C. Li, L. BAI, TimeKAN: KAN-based Frequency Decomposition Learning Architecture for Long-term Time Series Forecasting, in: The Thirteenth International Conference on Learning Representations."},{"key":"10.1016\/j.knosys.2026.116022_b64","unstructured":"Y. Nie, N.H. Nguyen, P. Sinthong, J. Kalagnanam, A Time Series is Worth 64 Words: Long-term Forecasting with Transformers, in: The Eleventh International Conference on Learning Representations."},{"key":"10.1016\/j.knosys.2026.116022_b65","series-title":"Non-stationary time series forecasting based on Fourier analysis and cross attention mechanism","author":"Xiong","year":"2025"},{"key":"10.1016\/j.knosys.2026.116022_b66","unstructured":"Y. Zhou, Y. Ye, P. Zhang, X. Du, M. Chen, TwinsFormer: Revisiting inherent dependencies via two interactive components for time series forecasting."},{"key":"10.1016\/j.knosys.2026.116022_b67","unstructured":"Y. Liu, T. Hu, H. Zhang, H. Wu, S. Wang, L. Ma, M. Long, iTransformer: Inverted Transformers Are Effective for Time Series Forecasting, in: The Twelfth International Conference on Learning Representations."},{"key":"10.1016\/j.knosys.2026.116022_b68","series-title":"Revisiting long-term time series forecasting: An investigation on linear mapping","author":"Li","year":"2023"},{"key":"10.1016\/j.knosys.2026.116022_b69","unstructured":"A. Das, W. Kong, A. Leach, S.K. Mathur, R. Sen, R. Yu, Long-term Forecasting with TiDE: Time-series Dense Encoder, Trans. Mach. Learn. Res.."}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126007483?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126007483?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T00:12:52Z","timestamp":1781223172000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0950705126007483"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":69,"alternative-id":["S0950705126007483"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116022","relation":{},"ISSN":["0950-7051"],"issn-type":[{"value":"0950-7051","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"DTFNet: A dual-modal time\u2013frequency fusion network for non-stationary time series modeling","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116022","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":"116022"}}