{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T19:17:41Z","timestamp":1783106261478,"version":"3.54.6"},"reference-count":37,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100012542","name":"Sichuan Provincial Science and Technology Support Program","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62272398"],"award-info":[{"award-number":["62272398"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2024NSFJQ0019"],"award-info":[{"award-number":["2024NSFJQ0019"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Information Fusion"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.inffus.2026.104407","type":"journal-article","created":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T05:58:46Z","timestamp":1777183126000},"page":"104407","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["ERIS : An energy-guided feature disentanglement framework for out-of-distribution time series classification"],"prefix":"10.1016","volume":"135","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-2292-4534","authenticated-orcid":false,"given":"Xin","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9535-7245","authenticated-orcid":false,"given":"Fei","family":"Teng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6949-3673","authenticated-orcid":false,"given":"Ji","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9650-130X","authenticated-orcid":false,"given":"Xingwang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2817-7337","authenticated-orcid":false,"given":"Yuxuan","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.inffus.2026.104407_bib0001","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.inffus.2023.02.023","article-title":"A novel distributed forecasting method based on information fusion and incremental learning for streaming time series","volume":"95","author":"Melgar-Garc\u00eda","year":"2023","journal-title":"Inf. Fusion"},{"issue":"4","key":"10.1016\/j.inffus.2026.104407_bib0002","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1007\/s43674-025-00084-y","article-title":"Forecasting China\u2019s producer price index for production materials via Gaussian process regression within a Bayesian inference framework","volume":"5","author":"Jin","year":"2025","journal-title":"Adv. Comput. Intell."},{"key":"10.1016\/j.inffus.2026.104407_bib0003","article-title":"A high-frequency trading volume prediction model using neural networks","volume":"7","author":"Xu","year":"2023","journal-title":"Decis. Anal. J."},{"key":"10.1016\/j.inffus.2026.104407_bib0004","series-title":"Proc. ACM SIGKDD Conf. Knowl. Discov. Data Min.","first-page":"3409","article-title":"Domain-specific risk minimization for domain generalization","author":"Zhang","year":"2023"},{"key":"10.1016\/j.inffus.2026.104407_bib0005","series-title":"Proc. ACM SIGKDD Conf. Knowl. Discov. Data Min.","first-page":"1615","article-title":"Environment agnostic invariant risk minimization for classification of sequential datasets","author":"Venkateswaran","year":"2021"},{"key":"10.1016\/j.inffus.2026.104407_bib0006","series-title":"Proc. ACM SIGKDD Conf. Knowl. Discov. Data Min.","first-page":"2674","article-title":"Orthogonality matters: invariant time series representation for out-of-distribution classification","author":"Shi","year":"2024"},{"key":"10.1016\/j.inffus.2026.104407_bib0007","series-title":"Proc. ACM SIGKDD Conf. Knowl. Discov. Data Min.","article-title":"HAROOD: a benchmark for out-of-distribution generalization in sensor-based human activity recognition","author":"Lu","year":"2026"},{"issue":"0","key":"10.1016\/j.inffus.2026.104407_bib0008","first-page":"1","article-title":"A tutorial on energy-based learning","volume":"1","author":"LeCun","year":"2006","journal-title":"Predict. Struct. Data 1"},{"key":"10.1016\/j.inffus.2026.104407_bib0009","series-title":"Proc. ACM SIGKDD Conf. Knowl. Discov. Data Min.","first-page":"5827","article-title":"Trustworthy machine learning: robustness, generalization, and interpretability","author":"Wang","year":"2023"},{"key":"10.1016\/j.inffus.2026.104407_bib0010","series-title":"Proc. Int. Conf. Learn. Represent.","article-title":"Distributionally robust neural networks","author":"Sagawa","year":"2020"},{"key":"10.1016\/j.inffus.2026.104407_bib0011","series-title":"Proc. Int. Conf. Mach. Learn.","first-page":"5815","article-title":"Out-of-distribution generalization via risk extrapolation (REX)","author":"Krueger","year":"2021"},{"key":"10.1016\/j.inffus.2026.104407_bib0012","unstructured":"M. Arjovsky, L. Bottou, I. Gulrajani, D. Lopez-Paz, Invariant risk minimization, (2019). 10.48550\/arXiv.1907.02893."},{"key":"10.1016\/j.inffus.2026.104407_bib0013","series-title":"Proc. AAAI Conf. Artif. Intell.","first-page":"10927","article-title":"Certifiable out-of-distribution generalization","volume":"37","author":"Ye","year":"2023"},{"key":"10.1016\/j.inffus.2026.104407_bib0014","series-title":"Proc. Adv. Neural Inf. Process. Syst.","first-page":"132908","article-title":"FOOGD: federated collaboration for both out-of-distribution generalization and detection","volume":"37","author":"Liao","year":"2024"},{"issue":"1","key":"10.1016\/j.inffus.2026.104407_bib0015","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1007\/s11263-024-02075-x","article-title":"Winning prize comes from losing tickets: improve invariant learning by exploring variant parameters for out-of-distribution generalization","volume":"133","author":"Huang","year":"2025","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.inffus.2026.104407_bib0016","article-title":"Matrix mixer analysis for time series classification: attention on tokenization","volume":"129","author":"Azizi","year":"2026","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.inffus.2026.104407_bib0017","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2025.103659","article-title":"MedViA: empowering medical time series classification with vision augmentation and multimodal fusion","volume":"127","author":"Fan","year":"2026","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.inffus.2026.104407_bib0018","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2025.103792","article-title":"Diffusion masked autoencoders as casual-aware curriculum learner for graph out-of-distribution generalization","volume":"127","author":"Liang","year":"2026","journal-title":"Inf. Fusion"},{"issue":"8","key":"10.1016\/j.inffus.2026.104407_bib0019","first-page":"8052","article-title":"Generalizing to unseen domains: a survey on domain generalization","volume":"35","author":"Wang","year":"2022","journal-title":"Trans. Knowl. Data Eng."},{"key":"10.1016\/j.inffus.2026.104407_bib0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2026.104336","article-title":"Out-of-distribution generalization in time series: a survey","volume":"133","author":"Wu","year":"2026","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.inffus.2026.104407_bib0021","series-title":"ACM Int. Conf. Inf. Knowl. Manag.","first-page":"402","article-title":"AdaRNN: adaptive learning and forecasting of time series","author":"Du","year":"2021"},{"key":"10.1016\/j.inffus.2026.104407_bib0022","series-title":"Proc. Int. Conf. Learn. Represent.","article-title":"Out-of-distribution representation learning for time series classification","author":"Lu","year":"2023"},{"key":"10.1016\/j.inffus.2026.104407_bib0023","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."},{"issue":"3","key":"10.1016\/j.inffus.2026.104407_bib0024","doi-asserted-by":"crossref","first-page":"3121","DOI":"10.1109\/TPAMI.2022.3181070","article-title":"GAN inversion: a survey","volume":"45","author":"Xia","year":"2023","journal-title":"Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.inffus.2026.104407_bib0025","doi-asserted-by":"crossref","DOI":"10.1109\/TMM.2026.3705189","article-title":"Exploring partial multi-label learning via integrating semantic co-occurrence knowledge","author":"Wu","year":"2026","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.inffus.2026.104407_bib0026","series-title":"Proc. IEEE\/CVF Int. Conf. Comput. Vis.","first-page":"9282","article-title":"Energy-based open-world uncertainty modeling for confidence calibration","author":"Wang","year":"2021"},{"key":"10.1016\/j.inffus.2026.104407_bib0027","series-title":"Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit.","first-page":"7452","article-title":"Energy-based latent aligner for incremental learning","author":"Joseph","year":"2022"},{"key":"10.1016\/j.inffus.2026.104407_bib0028","series-title":"Proc. IEEE\/CVF Int. Conf. Comput. Vis.","first-page":"22952","article-title":"EGC: image generation and classification via a diffusion energy-based model","author":"Guo","year":"2023"},{"key":"10.1016\/j.inffus.2026.104407_bib0029","series-title":"Proc. Adv. Neural Inf. Process. Syst.","first-page":"47966","article-title":"Enhancing consistency-based image generation via adversarialy-trained classification and energy-based discrimination","volume":"37","author":"Golan","year":"2024"},{"issue":"8","key":"10.1016\/j.inffus.2026.104407_bib0030","doi-asserted-by":"crossref","first-page":"1979","DOI":"10.1109\/TPAMI.2018.2858821","article-title":"Virtual adversarial training: a regularization method for supervised and semi-supervised learning","volume":"41","author":"Miyato","year":"2019","journal-title":"Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.inffus.2026.104407_bib0031","series-title":"Proc. ACM SIGKDD Conf. Knowl. Discov. Data Min.","first-page":"212","article-title":"TARNet: task-aware reconstruction for time-series transformer","author":"Chowdhury","year":"2022"},{"key":"10.1016\/j.inffus.2026.104407_bib0032","series-title":"Proc. Int. Conf. Learn. Represent.","article-title":"A time series is worth 64 words: long-term forecasting with transformers","author":"Nie","year":"2023"},{"key":"10.1016\/j.inffus.2026.104407_bib0033","series-title":"Proc. AAAI Conf. Artif. Intell.","first-page":"15725","article-title":"Graph-aware contrasting for multivariate time-series classification","volume":"38","author":"Wang","year":"2024"},{"key":"10.1016\/j.inffus.2026.104407_bib0034","series-title":"Proc. AAAI Conf. Artif. Intell.","first-page":"19572","article-title":"MPTSNet: integrating multiscale periodic local patterns and global dependencies for multivariate time series classification","volume":"39","author":"Mu","year":"2025"},{"key":"10.1016\/j.inffus.2026.104407_bib0035","article-title":"TSMixer: an all-MLP architecture for time series forecasting","author":"Chen","year":"2023","journal-title":"IEEE Trans. Mach. Learn. Res."},{"key":"10.1016\/j.inffus.2026.104407_bib0036","series-title":"Proc. Int. Conf. Learn. Represent.","article-title":"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling","author":"Bai","year":"2018"},{"key":"10.1016\/j.inffus.2026.104407_bib0037","unstructured":"Y. Wang, H. Wu, J. Dong, G. Qin, H. Zhang, Y. Liu, Y. Qiu, J. Wang, Long M., TimeXer: empowering transformers for time series forecasting with exogenous variables, in: Proc. Adv. Neural Inf. Process. Syst., 2024469\u2013498."}],"container-title":["Information Fusion"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1566253526002861?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1566253526002861?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T18:36:47Z","timestamp":1783103807000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1566253526002861"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":37,"alternative-id":["S1566253526002861"],"URL":"https:\/\/doi.org\/10.1016\/j.inffus.2026.104407","relation":{},"ISSN":["1566-2535"],"issn-type":[{"value":"1566-2535","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"ERIS : An energy-guided feature disentanglement framework for out-of-distribution time series classification","name":"articletitle","label":"Article Title"},{"value":"Information Fusion","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.inffus.2026.104407","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":"104407"}}