{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T12:09:23Z","timestamp":1784203763242,"version":"3.55.0"},"reference-count":54,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100013804","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013804","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["N25LPY049"],"award-info":[{"award-number":["N25LPY049"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.neucom.2026.134152","type":"journal-article","created":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T15:59:39Z","timestamp":1780329579000},"page":"134152","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["FD2E: Feature-enhanced driven diffusion expansion for single domain generalization in time series classification"],"prefix":"10.1016","volume":"696","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-6352-3887","authenticated-orcid":false,"given":"Mingpeng","family":"Zheng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyue","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quanzhi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"4","key":"10.1016\/j.neucom.2026.134152_bib0005","first-page":"4396","article-title":"Domain generalization: a survey","volume":"45","author":"Zhou","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"8","key":"10.1016\/j.neucom.2026.134152_bib0010","first-page":"8052","article-title":"Generalizing to unseen domains: a survey on domain generalization","volume":"35","author":"Wang","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.neucom.2026.134152_bib0015","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"12556","article-title":"Learning to learn single domain generalization","author":"Qiao","year":"2020"},{"key":"10.1016\/j.neucom.2026.134152_bib0020","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"834","article-title":"Learning to diversify for single domain generalization","author":"Wang","year":"2021"},{"key":"10.1016\/j.neucom.2026.134152_bib0025","series-title":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ACM","first-page":"3794","article-title":"Practical single domain generalization via training-time and test-time learning","author":"Yang","year":"2024"},{"key":"10.1016\/j.neucom.2026.134152_bib0030","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2025.130120","article-title":"Multi-receptive field feature disentanglement with distance-aware Gaussian brightness augmentation for single-source domain generalization in medical image segmentation","volume":"638","author":"Wang","year":"2025","journal-title":"Neurocomputing"},{"issue":"4","key":"10.1016\/j.neucom.2026.134152_bib0035","doi-asserted-by":"crossref","first-page":"1476","DOI":"10.3390\/s22041476","article-title":"Deep learning in human activity recognition with wearable sensors: a review on advances","volume":"22","author":"Zhang","year":"2022","journal-title":"Sensors"},{"issue":"20","key":"10.1016\/j.neucom.2026.134152_bib0040","doi-asserted-by":"crossref","first-page":"8016","DOI":"10.3390\/s22208016","article-title":"A systematic review of time series classification techniques used in biomedical applications","volume":"22","author":"Wang","year":"2022","journal-title":"Sensors"},{"issue":"8","key":"10.1016\/j.neucom.2026.134152_bib0045","doi-asserted-by":"crossref","first-page":"2655","DOI":"10.3390\/s24082655","article-title":"Prototype learning for medical time series classification via human\u2013machine collaboration","volume":"24","author":"Xie","year":"2024","journal-title":"Sensors"},{"issue":"7","key":"10.1016\/j.neucom.2026.134152_bib0050","doi-asserted-by":"crossref","first-page":"1807","DOI":"10.1007\/s10115-021-01569-1","article-title":"Feature extraction for chart pattern classification in financial time series","volume":"63","author":"Zheng","year":"2021","journal-title":"Knowl. Inf. Syst."},{"key":"10.1016\/j.neucom.2026.134152_bib0055","series-title":"Case-Based Reasoning Research and Development","first-page":"375","article-title":"A case-based reasoning approach to company sector classification using a novel time-series case representation","volume":"vol. 14141","author":"Dolphin","year":"2023"},{"key":"10.1016\/j.neucom.2026.134152_bib0060","series-title":"The Eleventh International Conference on Learning Representations","article-title":"Out-of-distribution representation learning for time series classification","author":"Lu","year":"2023"},{"key":"10.1016\/j.neucom.2026.134152_bib0065","series-title":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ACM","first-page":"1943","article-title":"Generalizable low-resource activity recognition with diverse and discriminative representation learning","author":"Qin","year":"2023"},{"issue":"5","key":"10.1016\/j.neucom.2026.134152_bib0070","doi-asserted-by":"crossref","first-page":"2338","DOI":"10.1109\/TBDATA.2025.3527215","article-title":"Generalized time series classification via component decomposition and alignment","volume":"11","author":"Cheng","year":"2025","journal-title":"IEEE Trans. Big Data"},{"key":"10.1016\/j.neucom.2026.134152_bib0075","series-title":"International Conference on Learning Representations","article-title":"mixup: beyond empirical risk minimization","author":"Zhang","year":"2018"},{"key":"10.1016\/j.neucom.2026.134152_bib0080","author":"Deng"},{"key":"10.1016\/j.neucom.2026.134152_bib0085","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.128889","article-title":"Domain generalization through latent distribution exploration for motor imagery EEG classification","volume":"614","author":"Song","year":"2025","journal-title":"Neurocomputing"},{"issue":"13","key":"10.1016\/j.neucom.2026.134152_bib0090","first-page":"11921","article-title":"Latent independent excitation for generalizable sensor-based cross-person activity recognition","volume":"35","author":"Qian","year":"2021","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"10.1016\/j.neucom.2026.134152_bib0095","series-title":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management, ACM, Virtual Event","first-page":"402","article-title":"AdaRNN: adaptive learning and forecasting of time series","author":"Du","year":"2021"},{"key":"10.1016\/j.neucom.2026.134152_bib0100","series-title":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ACM","first-page":"4213","article-title":"Diverse intra- and inter-domain activity style fusion for cross-person generalization in activity recognition","author":"Zhang","year":"2024"},{"key":"10.1016\/j.neucom.2026.134152_bib0105","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"224","article-title":"Progressive domain expansion network for single domain generalization","author":"Li","year":"2021"},{"key":"10.1016\/j.neucom.2026.134152_bib0110","series-title":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","first-page":"3764","article-title":"Diffusion-guided diversity for single domain generalization in time series classification","author":"Zhang","year":"2025"},{"issue":"2","key":"10.1016\/j.neucom.2026.134152_bib0115","first-page":"2366","article-title":"Rethinking data augmentation for single-source domain generalization in medical image segmentation","volume":"37","author":"Su","year":"2023","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"10.1016\/j.neucom.2026.134152_bib0120","series-title":"Web-Age Information Management","first-page":"298","article-title":"Time series classification using multi-channels deep convolutional neural networks","volume":"vol. 8485","author":"Zheng","year":"2014"},{"issue":"4","key":"10.1016\/j.neucom.2026.134152_bib0125","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1007\/s10618-019-00619-1","article-title":"Deep learning for time series classification: a review","volume":"33","author":"Fawaz","year":"2019","journal-title":"Data Min. Knowl. Discov."},{"key":"10.1016\/j.neucom.2026.134152_bib0130","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1016\/j.neucom.2019.06.032","article-title":"A deep learning framework for time series classification using relative position matrix and convolutional neural network","volume":"359","author":"Chen","year":"2019","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.134152_bib0135","series-title":"Machine Learning and Knowledge Discovery in Databases: Research Track","first-page":"189","article-title":"Adacket: ADAptive convolutional KErnel transform for multivariate time series classification","volume":"vol. 14173","author":"Zhang","year":"2023"},{"issue":"14","key":"10.1016\/j.neucom.2026.134152_bib0140","first-page":"15725","article-title":"Graph-aware contrasting for multivariate time-series classification","volume":"38","author":"Wang","year":"2024","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"issue":"14","key":"10.1016\/j.neucom.2026.134152_bib0145","first-page":"15715","article-title":"Fully-connected spatial-temporal graph for multivariate time-series data","volume":"38","author":"Wang","year":"2024","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"10.1016\/j.neucom.2026.134152_bib0150","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.neucom.2019.10.113","article-title":"Combining contextual neural networks for time series classification","volume":"384","author":"Kamara","year":"2020","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.134152_bib0155","series-title":"Proceedings of the 31st ACM International Conference on Multimedia, ACM","first-page":"883","article-title":"ASTDF-Net: attention-based spatial-temporal dual-stream fusion network for EEG-based emotion recognition","author":"Gong","year":"2023"},{"key":"10.1016\/j.neucom.2026.134152_bib0160","series-title":"Proceedings of the ACM Web Conference 2023, ACM","first-page":"1437","article-title":"FormerTime: hierarchical multi-scale representations for multivariate time series classification","author":"Cheng","year":"2023"},{"key":"10.1016\/j.neucom.2026.134152_bib0165","series-title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence Organization","first-page":"2352","article-title":"Time-series representation learning via temporal and contextual contrasting","author":"Eldele","year":"2021"},{"key":"10.1016\/j.neucom.2026.134152_bib0170","series-title":"Computer Vision \u2013 ECCV 2016 Workshops","first-page":"443","article-title":"Deep coral: correlation alignment for deep domain adaptation","volume":"vol. 9915","author":"Sun","year":"2016"},{"issue":"4","key":"10.1016\/j.neucom.2026.134152_bib0175","first-page":"6502","article-title":"Adversarial domain adaptation with domain mixup","volume":"34","author":"Xu","year":"2020","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"10.1016\/j.neucom.2026.134152_bib0180","doi-asserted-by":"crossref","DOI":"10.52202\/079017-3659","article-title":"PowerPM: foundation model for power systems","volume":"37","author":"Tu","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2026.134152_bib0185","series-title":"Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing","first-page":"28208","article-title":"Scaling up temporal domain generalization via temporal experts averaging","author":"Liu","year":"2025"},{"issue":"14","key":"10.1016\/j.neucom.2026.134152_bib0190","doi-asserted-by":"crossref","first-page":"4434","DOI":"10.3390\/s25144434","article-title":"Meta-learning task relations for ensemble-based temporal domain generalization in sensor data forecasting","volume":"25","author":"Zhang","year":"2025","journal-title":"Sensors"},{"key":"10.1016\/j.neucom.2026.134152_bib0195","series-title":"Proceedings of the 31st ACM International Conference on Multimedia, ACM","first-page":"2101","article-title":"Single domain generalization via unsupervised diversity probe","author":"Guo","year":"2023"},{"key":"10.1016\/j.neucom.2026.134152_bib0200","series-title":"Proceedings of the 32nd International Conference on Machine Learning, Vol. 37 of Proceedings of Machine Learning Research","first-page":"2256","article-title":"Deep unsupervised learning using nonequilibrium thermodynamics","author":"Sohl-Dickstein","year":"2015"},{"key":"10.1016\/j.neucom.2026.134152_bib0205","series-title":"Denoising Diffusion Probabilistic Models","author":"Ho","year":"2020"},{"issue":"6","key":"10.1016\/j.neucom.2026.134152_bib0210","doi-asserted-by":"crossref","first-page":"2769","DOI":"10.1214\/009053607000000505","article-title":"Measuring and testing dependence by correlation of distances","volume":"35","author":"Sz\u00e9kely","year":"2007","journal-title":"Ann. Stat."},{"key":"10.1016\/j.neucom.2026.134152_bib0215","series-title":"Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research","first-page":"531","article-title":"Mutual information neural estimation","author":"Belghazi","year":"2018"},{"issue":"25","key":"10.1016\/j.neucom.2026.134152_bib0220","first-page":"723","article-title":"A kernel two-sample test","volume":"13","author":"Gretton","year":"2012","journal-title":"J. Mach. Learn. Res."},{"issue":"59","key":"10.1016\/j.neucom.2026.134152_bib0225","first-page":"1","article-title":"Domain-adversarial training of neural networks","volume":"17","author":"Ganin","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.neucom.2026.134152_bib0230","series-title":"Algorithmic Learning Theory","first-page":"63","article-title":"Measuring statistical dependence with Hilbert-schmidt norms","volume":"vol. 3734","author":"Gretton","year":"2005"},{"key":"10.1016\/j.neucom.2026.134152_bib0235","series-title":"Proceedings of the 41st International Conference on Machine Learning, Vol. 235 of Proceedings of Machine Learning Research","first-page":"20452","article-title":"InterLUDE: interactions between labeled and unlabeled data to enhance semi-supervised learning","author":"Huang","year":"2024"},{"key":"10.1016\/j.neucom.2026.134152_bib0240","series-title":"The Twelfth International Conference on Learning Representations","article-title":"Universal guidance for diffusion models","author":"Bansal","year":"2024"},{"issue":"4","key":"10.1016\/j.neucom.2026.134152_bib0245","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.3390\/s18041122","article-title":"Latent factors limiting the performance of sEMG-Interfaces","volume":"18","author":"Lobov","year":"2018","journal-title":"Sensors"},{"key":"10.1016\/j.neucom.2026.134152_bib0250","unstructured":"B. Barshan, K. Altun, Daily and sports activities, UCI Machine Learning Repository, Dataset, 2010, 10.24432\/C5C59F"},{"key":"10.1016\/j.neucom.2026.134152_bib0255","series-title":"2012 16th International Symposium on Wearable Computers","first-page":"108","article-title":"Introducing a new benchmarked dataset for activity monitoring","author":"Reiss","year":"2012"},{"key":"10.1016\/j.neucom.2026.134152_bib0260","series-title":"Proceedings of the 2012 ACM Conference on Ubiquitous Computing","first-page":"1036","article-title":"USC-HAD: a daily activity dataset for ubiquitous activity recognition using wearable sensors","author":"Zhang","year":"2012"},{"key":"10.1016\/j.neucom.2026.134152_bib0265","series-title":"International Conference on Learning Representations","article-title":"Learning explanations that are hard to vary","author":"Parascandolo","year":"2021"},{"key":"10.1016\/j.neucom.2026.134152_bib0270","series-title":"Computer Vision \u2013 ECCV 2020","first-page":"124","article-title":"Self-challenging improves cross-domain generalization","volume":"vol. 12347","author":"Huang","year":"2020"}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S092523122601550X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S092523122601550X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T11:44:57Z","timestamp":1784202297000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S092523122601550X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":54,"alternative-id":["S092523122601550X"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2026.134152","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"FD2E: Feature-enhanced driven diffusion expansion for single domain generalization in time series classification","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2026.134152","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":"134152"}}