{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T16:28:54Z","timestamp":1784737734817,"version":"3.55.0"},"reference-count":192,"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\/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\/100012542","name":"Sichuan Province Science and Technology Support Program","doi-asserted-by":"publisher","award":["2024NSFJQ0019"],"award-info":[{"award-number":["2024NSFJQ0019"]}],"id":[{"id":"10.13039\/100012542","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":[[2026,9]]},"DOI":"10.1016\/j.inffus.2026.104336","type":"journal-article","created":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T15:53:22Z","timestamp":1775231602000},"page":"104336","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":7,"special_numbering":"C","title":["Out-of-Distribution Generalization in Time Series: A Survey"],"prefix":"10.1016","volume":"133","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\/0009-0007-9650-130X","authenticated-orcid":false,"given":"Xingwang","family":"Li","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\/0000-0001-7832-1937","authenticated-orcid":false,"given":"Qiang","family":"Duan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7780-104X","authenticated-orcid":false,"given":"Tianrui","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/j.inffus.2026.104336_bib0001","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1016\/j.ejor.2017.11.054","article-title":"Deep learning with long short-term memory networks for financial market predictions","volume":"270","author":"Fischer","year":"2018","journal-title":"Eur. J. Oper. Res."},{"issue":"5","key":"10.1016\/j.inffus.2026.104336_bib0002","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1109\/TKDE.2006.80","article-title":"Pattern discovery of fuzzy time series for financial prediction","volume":"18","author":"Lee","year":"2006","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"3","key":"10.1016\/j.inffus.2026.104336_bib0003","doi-asserted-by":"crossref","first-page":"4765","DOI":"10.1109\/TII.2023.3326546","article-title":"Integration of computer vision and IOT into an automatic driving assistance system for \u201celectric Vehicles\u201d","volume":"20","author":"Du","year":"2024","journal-title":"IEEE Trans. Ind. Inf."},{"issue":"3","key":"10.1016\/j.inffus.2026.104336_bib0004","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1007\/s10462-016-9467-9","article-title":"Artificial intelligence techniques for driving safety and vehicle crash prediction","volume":"46","author":"Halim","year":"2016","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.inffus.2026.104336_bib0005","article-title":"Medvia: empowering medical time series classification with vision augmentation and multimodal fusion","author":"Fan","year":"2025","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.inffus.2026.104336_bib0006","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.inffus.2019.06.014","article-title":"Imaging and fusing time series for wearable sensor-based human activity recognition","volume":"53","author":"Qin","year":"2020","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.inffus.2026.104336_bib0007","doi-asserted-by":"crossref","unstructured":"X. Wu, F. Teng, J. Zhang, X. Li, Y. Liang, ERIS: An Energy-Guided Feature Disentanglement Framework for Out-of-Distribution Time Series Classification, 2025. 10.48550\/arXiv.2508.14134.","DOI":"10.2139\/ssrn.6075176"},{"key":"10.1016\/j.inffus.2026.104336_bib0008","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.2026.104336_bib0009","series-title":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"2565","article-title":"RAIM: Recurrent attentive and intensive model of multimodal patient monitoring data","author":"Xu","year":"2018"},{"issue":"4","key":"10.1016\/j.inffus.2026.104336_bib0010","doi-asserted-by":"crossref","first-page":"4293","DOI":"10.1109\/TKDE.2021.3140058","article-title":"Time series anomaly detection with adversarial reconstruction networks","volume":"35","author":"Liu","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"4","key":"10.1016\/j.inffus.2026.104336_bib0011","doi-asserted-by":"crossref","first-page":"2609","DOI":"10.1007\/s10462-020-09910-w","article-title":"Role of artificial intelligence in rotor fault diagnosis: a comprehensive review","volume":"54","author":"Nath","year":"2021","journal-title":"Artif. Intell. Rev."},{"issue":"3","key":"10.1016\/j.inffus.2026.104336_bib0012","doi-asserted-by":"crossref","first-page":"1478","DOI":"10.1109\/TMECH.2021.3087503","article-title":"Multiple-Timescale feature learning strategy for valve stiction detection based on convolutional neural network","volume":"27","author":"Zhang","year":"2022","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"10.1016\/j.inffus.2026.104336_bib0013","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.inffus.2012.09.001","article-title":"Information fusion in wireless sensor networks with source correlation","volume":"15","author":"Ferrari","year":"2014","journal-title":"Inf. fusion"},{"key":"10.1016\/j.inffus.2026.104336_bib0014","doi-asserted-by":"crossref","first-page":"2366","DOI":"10.1109\/TKDE.2026.3658637","article-title":"Preference guided meta-Learning for cross domain time series forecasting","volume":"38","author":"Li","year":"2026","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"8","key":"10.1016\/j.inffus.2026.104336_bib0015","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.inffus.2026.104336_bib0016","article-title":"Diffusion masked autoencoders as casual-aware curriculum learner for graph out-of-Distribution generalization","author":"Liang","year":"2025","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.inffus.2026.104336_bib0017","article-title":"Towards enhanced LLM pretraining: dynamic checkpoint merging via generation quality","author":"Wang","year":"2025","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.inffus.2026.104336_bib0018","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2025.103021","article-title":"NeuralOOD: improving out-of-distribution generalization performance with brain-machine fusion learning framework","volume":"119","author":"Zhao","year":"2025","journal-title":"Inf. Fusion"},{"issue":"4","key":"10.1016\/j.inffus.2026.104336_bib0019","doi-asserted-by":"crossref","DOI":"10.1007\/s10462-025-11116-x","article-title":"Advances in diffusion models for image data augmentation: a review of methods, models, evaluation metrics and future research directions","volume":"58","author":"Alimisis","year":"2025","journal-title":"Artif. Intell. Rev."},{"issue":"10","key":"10.1016\/j.inffus.2026.104336_bib0020","doi-asserted-by":"crossref","DOI":"10.1007\/s10462-024-10922-z","article-title":"Domain generalization through meta-learning: a survey","volume":"57","author":"Khoee","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.inffus.2026.104336_bib0021","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2024.102780","article-title":"Large model-driven hyperscale healthcare data fusion analysis in complex multi-sensors","volume":"115","author":"Lv","year":"2025","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.inffus.2026.104336_bib0022","unstructured":"K. Tang, J. Zhang, H. Meng, M. Ma, Q. Xiong, F. Lv, J. Xu, T. Li, CoIFNet: A Unified Framework for Multivariate Time Series Forecasting with Missing Values, arXiv: 2506.13064 (2025)."},{"issue":"4","key":"10.1016\/j.inffus.2026.104336_bib0023","doi-asserted-by":"crossref","first-page":"7287","DOI":"10.1109\/TNNLS.2024.3384842","article-title":"Distributional drift adaptation with temporal conditional variational autoencoder for multivariate time series forecasting","volume":"36","author":"He","year":"2025","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.inffus.2026.104336_bib0024","series-title":"Proceedings of the 30th ACM International Conference on Multimedia","first-page":"5934","article-title":"Domain adaptation for time-Series classification to mitigate covariate shift","author":"Ott","year":"2022"},{"issue":"10, Part B","key":"10.1016\/j.inffus.2026.104336_bib0025","doi-asserted-by":"crossref","first-page":"9608","DOI":"10.1016\/j.jksuci.2021.11.014","article-title":"IoT-Edge anomaly detection for covariate shifted and point time series health data","volume":"34","author":"Ray","year":"2022","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"issue":"3","key":"10.1016\/j.inffus.2026.104336_bib0026","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1016\/j.inffus.2005.05.005","article-title":"Real-time data mining of non-stationary data streams from sensor networks","volume":"9","author":"Cohen","year":"2008","journal-title":"Inf. Fusion"},{"issue":"12","key":"10.1016\/j.inffus.2026.104336_bib0027","doi-asserted-by":"crossref","DOI":"10.1145\/3737279","article-title":"Supervised learning from data streams: an overview and update","volume":"57","author":"Read","year":"2025","journal-title":"ACM Comput. Surv."},{"issue":"9","key":"10.1016\/j.inffus.2026.104336_bib0028","doi-asserted-by":"crossref","DOI":"10.1145\/3472752","article-title":"A survey on concept drift in process mining","volume":"54","author":"Sato","year":"2021","journal-title":"ACM Comput. Surv."},{"issue":"12","key":"10.1016\/j.inffus.2026.104336_bib0029","doi-asserted-by":"crossref","first-page":"7300","DOI":"10.1109\/TKDE.2025.3609415","article-title":"Modeling temporal dependencies within the target for long-Term time series forecasting","volume":"37","author":"Xiong","year":"2025","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.inffus.2026.104336_bib0030","series-title":"Proceedings of the 30th ACM International Conference on Information and Knowledge Management","first-page":"2434","article-title":"Learning to learn the future: modeling concept drifts in time series prediction","author":"You","year":"2021"},{"key":"10.1016\/j.inffus.2026.104336_bib0031","series-title":"Proceedings of the 41St International Conference on Machine Learning","article-title":"Time-series forecasting for out-of-distribution generalization using invariant learning","author":"Liu","year":"2024"},{"key":"10.1016\/j.inffus.2026.104336_bib0032","series-title":"Proceedings of the 9th International Conference on Learning Representations","article-title":"The risks of invariant risk minimization","author":"Rosenfeld","year":"2021"},{"key":"10.1016\/j.inffus.2026.104336_bib0033","series-title":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","first-page":"341","article-title":"Calibration of time-Series forecasting: detecting and adapting context-Driven distribution shift","author":"Chen","year":"2024"},{"key":"10.1016\/j.inffus.2026.104336_bib0034","unstructured":"A. Garza, C. Challu, M. Mergenthaler-Canseco, TimeGPT-1, 2024. 10.48550\/arXiv.2310.03589."},{"key":"10.1016\/j.inffus.2026.104336_bib0035","unstructured":"J. Liu, Z. Shen, Y. He, X. Zhang, R. Xu, H. Yu, P. Cui, Towards Out-Of-Distribution Generalization: A Survey, 2023. 10.48550\/arXiv.2108.13624."},{"issue":"11","key":"10.1016\/j.inffus.2026.104336_bib0036","doi-asserted-by":"crossref","first-page":"10490","DOI":"10.1109\/TPAMI.2025.3593897","article-title":"Out-of-Distribution generalization on graphs: a survey","volume":"47","author":"Li","year":"2025","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.inffus.2026.104336_bib0037","series-title":"Proceedings of the 27th Conference on Empirical Methods in Natural Language Processing","first-page":"4533","article-title":"Out-of-Distribution generalization in natural language processing: past, present, and future","author":"Yang","year":"2023"},{"key":"10.1016\/j.inffus.2026.104336_bib0038","series-title":"Proceedings of the 32Nd IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"113","article-title":"Autoaugment: learning augmentation strategies from data","author":"Cubuk","year":"2019"},{"issue":"7","key":"10.1016\/j.inffus.2026.104336_bib0039","first-page":"2973","article-title":"Neural decomposition of time-Series data for effective generalization","volume":"29","author":"Godfrey","year":"2018","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.inffus.2026.104336_bib0040","series-title":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","first-page":"2437","article-title":"Multilevel wavelet decomposition network for interpretable time series analysis","author":"Wang","year":"2018"},{"key":"10.1016\/j.inffus.2026.104336_bib0041","article-title":"Batch training for streaming time series: a transferable augmentation framework to combat distribution shifts","author":"Zhang","year":"2025","journal-title":"Trans. Mach. Learn. Res."},{"key":"10.1016\/j.inffus.2026.104336_bib0042","series-title":"Proceedings of the 32Nd IEEE International Conference on Fuzzy Systems","first-page":"1","article-title":"An initial step towards stable explanations for multivariate time series classifiers with LIME","author":"Meng","year":"2023"},{"issue":"C","key":"10.1016\/j.inffus.2026.104336_bib0043","article-title":"SEGAL time series classification \u2014 Stable explanations using a generative model and an adaptive weighting method for LIME","volume":"176","author":"Meng","year":"2024","journal-title":"Neural Netw."},{"key":"10.1016\/j.inffus.2026.104336_bib0044","article-title":"Electricity consumption forecasting for out-of-Distribution time-of-Use tariffs","volume":"3","author":"Narwariya","year":"2022","journal-title":"Comput. Sci. Math. Forum"},{"key":"10.1016\/j.inffus.2026.104336_bib0045","series-title":"Proceedings of the 31St IEEE Conference Virtual Reality and 3D User Interfaces","first-page":"732","article-title":"Generating virtual reality stroke gesture data from out-of-Distribution desktop stroke gesture data","author":"Yuan","year":"2024"},{"key":"10.1016\/j.inffus.2026.104336_bib0046","series-title":"Proceedings of the 38th AAAI Conference on Artificial Intelligence","first-page":"16651","article-title":"Generalizing across temporal domains with koopman operators","volume":"38","author":"Zeng","year":"2024"},{"key":"10.1016\/j.inffus.2026.104336_bib0047","series-title":"Proceedings of the 33Rd International Joint Conference on Artificial Intelligence","article-title":"Temporal domain generalization via learning instance-level evolving patterns","author":"Jin","year":"2024"},{"key":"10.1016\/j.inffus.2026.104336_bib0048","series-title":"Proceedings of the 10th International Conference on Learning Representations","article-title":"Reversible instance normalization for accurate time-Series forecasting against distribution shift","author":"Kim","year":"2022"},{"key":"10.1016\/j.inffus.2026.104336_bib0049","series-title":"Proceedings of the 41St International Conference on Machine Learning","article-title":"Connect later: improving fine-tuning for robustness with targeted augmentations","author":"Qu","year":"2024"},{"key":"10.1016\/j.inffus.2026.104336_bib0050","series-title":"Proceedings of the 41St International Conference on Machine Learning","article-title":"TIMEX++: Learning time-series explanations with information bottleneck","author":"Liu","year":"2024"},{"key":"10.1016\/j.inffus.2026.104336_bib0051","series-title":"Proceedings of the 39th Conference on Neural Information Processing Systems","article-title":"Learning pattern-Specific experts for time series forecasting under patch-level distribution shift","author":"Sun","year":"2025"},{"key":"10.1016\/j.inffus.2026.104336_bib0052","series-title":"Proceedings of the 14th International Conference on Learning Representations","article-title":"The forecast after the forecast: a post-Processing shift in time series","author":"Liang","year":"2026"},{"key":"10.1016\/j.inffus.2026.104336_bib0053","series-title":"Proceedings of the 31St ACM SIGKDD Conference on Knowledge Discovery and Data Mining v.1","first-page":"295","article-title":"IN-Flow: Instance normalization flow for non-stationary time series forecasting","author":"Fan","year":"2025"},{"key":"10.1016\/j.inffus.2026.104336_bib0054","series-title":"Proceedings of the 28th International Conference on Parallel and Distributed System","first-page":"900","article-title":"Combating distribution shift for accurate time series forecasting via hypernetworks","author":"Duan","year":"2023"},{"key":"10.1016\/j.inffus.2026.104336_bib0055","series-title":"Proceedings of the 39th International Conference on Machine Learning Workshop on Principles of Distribution Shift","article-title":"Time series prediction under distribution shift using differentiable forgetting","author":"Bennett","year":"2022"},{"key":"10.1016\/j.inffus.2026.104336_bib0056","first-page":"1","article-title":"Robust multivariate time series forecasting against intraseries and interseries transitional shift","author":"He","year":"2025","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.inffus.2026.104336_bib0057","series-title":"Proceedings of the 34th International Conference on Neural Information Processing Systems","article-title":"Feature shift detection: localizing which features have shifted via conditional distribution tests","author":"Kulinski","year":"2020"},{"key":"10.1016\/j.inffus.2026.104336_bib0058","series-title":"Proceedings of the 13th International Conference on Learning Representations","article-title":"Minimax optimal two-Stage algorithm for moment estimation under covariate shift","author":"Zhang","year":"2025"},{"key":"10.1016\/j.inffus.2026.104336_bib0059","series-title":"Proceedings of the 12th International Conference on Learning Representations","article-title":"Disentangling time series representations via contrastive independence-of-Support on l-Variational inference","author":"Oublal","year":"2024"},{"key":"10.1016\/j.inffus.2026.104336_bib0060","series-title":"Proceedings of the 31St ACM SIGKDD Conference on Knowledge Discovery and Data Mining v.2","first-page":"991","article-title":"Metaeformer: unveiling and leveraging meta-Patterns for complex and dynamic systems load forecasting","author":"Huang","year":"2025"},{"key":"10.1016\/j.inffus.2026.104336_bib0061","unstructured":"J. Read, Concept-drifting Data Streams are Time Series; The Case for Continuous Adaptation, 2018. 10.48550\/arXiv.1810.02266."},{"key":"10.1016\/j.inffus.2026.104336_bib0062","series-title":"2025 International Conference on Intelligent Computing","article-title":"Alleviating distribution shift in time series forecasting with an invertible neural network transformation","author":"Deng","year":"2025"},{"key":"10.1016\/j.inffus.2026.104336_bib0063","series-title":"Proceedings of the 37th International Conference on Neural Information Processing Systems","article-title":"Onenet: enhancing time series forecasting models under concept drift by online ensembling","author":"Zhang","year":"2023"},{"key":"10.1016\/j.inffus.2026.104336_bib0064","series-title":"Proceedings of the 41St International Conference on Machine Learning","article-title":"CATS: Enhancing multivariate time series forecasting by constructing auxiliary time series as exogenous variables","author":"Lu","year":"2024"},{"key":"10.1016\/j.inffus.2026.104336_bib0065","series-title":"Proceedings of the 36th International Conference on Neural Information Processing Systems","article-title":"Dynamic graph neural networks under spatio-temporal distribution shift","author":"Zhang","year":"2022"},{"key":"10.1016\/j.inffus.2026.104336_bib0066","series-title":"NeurIPS 2022 Workshop on Distribution Shifts: Connecting Methods and Applications","article-title":"Adaptive sampling for probabilistic forecasting under distribution shift","author":"Masserano","year":"2022"},{"key":"10.1016\/j.inffus.2026.104336_bib0067","doi-asserted-by":"crossref","unstructured":"Y. Dong, J. Wu, Rethinking Adam for Time Series Forecasting: A Simple Heuristic to Improve Optimization under Distribution Shifts, Available at SSRN 5570331 (2026). 10.2139\/ssrn.5570331.","DOI":"10.2139\/ssrn.5570331"},{"issue":"4","key":"10.1016\/j.inffus.2026.104336_bib0068","doi-asserted-by":"crossref","first-page":"1932","DOI":"10.1109\/TPAMI.2023.3304354","article-title":"Transferable time-Series forecasting under causal conditional shift","volume":"46","author":"Li","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.inffus.2026.104336_bib0069","first-page":"1","article-title":"Long-Term urban flow prediction against data distribution shift: a causal perspective","author":"Liu","year":"2025","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.inffus.2026.104336_bib0070","series-title":"NeurIPS Workshop on Time Series in the Age of Large Models","article-title":"KAN4Drift: Are KAN effective for identifying and tracking concept drift in time series?","author":"Xu","year":"2024"},{"key":"10.1016\/j.inffus.2026.104336_bib0071","series-title":"Proceedings of the 31St ACM SIGKDD Conference on Knowledge Discovery and Data Mining v.1","first-page":"2020","article-title":"Proactive model adaptation against concept drift for online time series forecasting","author":"Zhao","year":"2025"},{"key":"10.1016\/j.inffus.2026.104336_bib0072","series-title":"Proceedings of the 33Rd AAAI Conference on Artificial Intelligence","article-title":"Cogra: concept-drift-aware stochastic gradient descent for time-series forecasting","author":"Miyaguchi","year":"2019"},{"issue":"7","key":"10.1016\/j.inffus.2026.104336_bib0073","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."},{"key":"10.1016\/j.inffus.2026.104336_bib0074","doi-asserted-by":"crossref","unstructured":"K. Xu, L. Chen, S. Wang, CORAL: Concept Drift Representation Learning for Co-evolving Time-series, 2025. 10.48550\/arXiv.2501.01480.","DOI":"10.1007\/978-981-95-0129-8_19"},{"key":"10.1016\/j.inffus.2026.104336_bib0075","series-title":"Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval","first-page":"252","article-title":"Tackling concept drift by temporal inductive transfer","author":"Forman","year":"2006"},{"issue":"6","key":"10.1016\/j.inffus.2026.104336_bib0076","doi-asserted-by":"crossref","DOI":"10.1007\/s42979-025-04247-z","article-title":"Dynamic swarm intelligence for time series forecasting in the presence of concept drift","volume":"6","author":"Oliveira","year":"2025","journal-title":"SN Comput. Sci."},{"issue":"10","key":"10.1016\/j.inffus.2026.104336_bib0077","doi-asserted-by":"crossref","first-page":"6000","DOI":"10.1109\/TCYB.2024.3429459","article-title":"TS-DM: A time segmentation-based data stream learning method for concept drift adaptation","volume":"54","author":"Wang","year":"2024","journal-title":"IEEE Trans. Cybern."},{"issue":"9","key":"10.1016\/j.inffus.2026.104336_bib0078","doi-asserted-by":"crossref","first-page":"2925","DOI":"10.1007\/s13042-023-01810-z","article-title":"Catsight, a direct path to proper multi-variate time series change detection: perceiving a concept drift through common spatial pattern","volume":"14","author":"Florez","year":"2023","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"10.1016\/j.inffus.2026.104336_bib0079","series-title":"2017 IEEE 29th International Conference on Tools with Artificial Intelligence","first-page":"239","article-title":"Time series forecasting in the presence of concept drift: a PSO-based approach","author":"Gustavo","year":"2017"},{"key":"10.1016\/j.inffus.2026.104336_bib0080","series-title":"2025 International Conference on Advancements in Power, Communication and Intelligent Systems","first-page":"1","article-title":"A novel concept drift detection model for handling evolving patterns in multivariate time series","author":"K","year":"2025"},{"key":"10.1016\/j.inffus.2026.104336_bib0081","series-title":"Proceedings of the 39th Conference on Neural Information Processing Systems","article-title":"How different from the past? spatio-Temporal time series forecasting with self-Supervised deviation learning","author":"Gao","year":"2025"},{"key":"10.1016\/j.inffus.2026.104336_bib0082","unstructured":"T. Zhan, M. Jin, Y. He, Y. Liang, Y. Deng, S. Pan, Continuous Evolution Pool: Taming Recurring Concept Drift in Online Time Series Forecasting, 2025. 10.48550\/arXiv.2506.14790."},{"key":"10.1016\/j.inffus.2026.104336_bib0083","series-title":"Proceedings of the 14th International Conference on Learning Representations","article-title":"AP-OOD: Attention pooling for out-of- distribution detection","author":"Hofmann","year":"2026"},{"key":"10.1016\/j.inffus.2026.104336_bib0084","series-title":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","first-page":"2674","article-title":"Orthogonality matters: invariant time series representation for out-of-distribution classification","author":"Shi","year":"2024"},{"issue":"8","key":"10.1016\/j.inffus.2026.104336_bib0085","doi-asserted-by":"crossref","first-page":"3783","DOI":"10.1109\/TKDE.2024.3371931","article-title":"Disentangling structured components: towards adaptive, interpretable and scalable time series forecasting","volume":"36","author":"Deng","year":"2024","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"2","key":"10.1016\/j.inffus.2026.104336_bib0086","doi-asserted-by":"crossref","DOI":"10.1145\/3491243","article-title":"Efficient out-of-Distribution detection using latent space of \u03b2-VAE for cyber-Physical systems","volume":"6","author":"Ramakrishna","year":"2022","journal-title":"ACM Trans. Cyber Phys. Syst."},{"key":"10.1016\/j.inffus.2026.104336_bib0087","series-title":"Proceedings of the 38th AAAI Conference on Artificial Intelligence","first-page":"11141","article-title":"MSGNet: Learning multi-scale inter-series correlations for multivariate time series forecasting","volume":"38","author":"Cai","year":"2024"},{"key":"10.1016\/j.inffus.2026.104336_bib0088","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","volume":"162","author":"Zhou","year":"2022"},{"key":"10.1016\/j.inffus.2026.104336_bib0089","series-title":"Proceedings of the 13th International Conference on Learning Representations","article-title":"SimpleTM: a simple baseline for multivariate time series forecasting","author":"Chen","year":"2025"},{"key":"10.1016\/j.inffus.2026.104336_bib0090","series-title":"Proceedings of the 39th International Conference on Robotics and Automation","first-page":"7806","article-title":"Causal-based time series domain generalization for vehicle intention prediction","author":"Hu","year":"2022"},{"key":"10.1016\/j.inffus.2026.104336_bib0091","series-title":"Proceedings of the 40th International Conference on Machine Learning","article-title":"Neural stochastic differential games for time-series analysis","author":"Park","year":"2023"},{"issue":"77","key":"10.1016\/j.inffus.2026.104336_bib0092","article-title":"Robust flight navigation out of distribution with liquid neural networks","volume":"8","author":"Chahine","year":"2023","journal-title":"Sci. Rob."},{"issue":"C","key":"10.1016\/j.inffus.2026.104336_bib0093","article-title":"A causal representation learning based model for time series prediction under external interference","volume":"663","author":"Feng","year":"2024","journal-title":"Inf. Sci."},{"key":"10.1016\/j.inffus.2026.104336_bib0094","series-title":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","first-page":"3603","article-title":"Maintaining the status quo: capturing invariant relations for OOD spatiotemporal learning","author":"Zhou","year":"2023"},{"key":"10.1016\/j.inffus.2026.104336_bib0095","series-title":"Proceedings of the 40th IEEE International Conference on Robotics and Automation","first-page":"5566","article-title":"Robust forecasting for robotic control: a game-Theoretic approach","author":"Agarwal","year":"2023"},{"key":"10.1016\/j.inffus.2026.104336_bib0096","series-title":"Companion Proceedings of the 33Rd ACM Web Conference","first-page":"1344","article-title":"Towards invariant time series forecasting in smart cities","author":"Zhang","year":"2024"},{"key":"10.1016\/j.inffus.2026.104336_bib0097","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.inffus.2026.104336_bib0098","series-title":"Proceedings of the 13th International Conference on Learning Representations","article-title":"Out-of-distribution generalization for total variation based invariant risk minimization","author":"Wang","year":"2025"},{"key":"10.1016\/j.inffus.2026.104336_bib0099","series-title":"Proceedings of the 23Rd IEEE International Conference on Data Mining (ICDM)","first-page":"160","article-title":"Boosting urban prediction via addressing spatial-Temporal distribution shift","author":"Hu","year":"2023"},{"issue":"5","key":"10.1016\/j.inffus.2026.104336_bib0100","doi-asserted-by":"crossref","DOI":"10.1145\/3643035","article-title":"Domain generalization in time series forecasting","volume":"18","author":"Deng","year":"2024","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"10.1016\/j.inffus.2026.104336_bib0101","series-title":"Proceedings of the 38th International Conference on Neural Information Processing Systems","article-title":"Continuous temporal domain generalization","author":"Cai","year":"2024"},{"key":"10.1016\/j.inffus.2026.104336_bib0102","series-title":"Proceedings of the 11th International Conference on Learning Representations","article-title":"Out-of-distribution representation learning for time series classification","author":"Lu","year":"2023"},{"issue":"6","key":"10.1016\/j.inffus.2026.104336_bib0103","doi-asserted-by":"crossref","first-page":"4534","DOI":"10.1109\/TPAMI.2024.3355212","article-title":"Diversify: a general framework for time series out-of-Distribution detection and generalization","volume":"46","author":"Lu","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.inffus.2026.104336_bib0104","doi-asserted-by":"crossref","DOI":"10.1016\/j.istruc.2024.106766","article-title":"Enhancing time series data classification for structural damage detection through out-of-distribution representation learning","volume":"65","author":"Tien","year":"2024","journal-title":"Structures"},{"key":"10.1016\/j.inffus.2026.104336_bib0105","series-title":"Proceedings of the 9th International Conference on Learning Representations","article-title":"In-n-out: pre-training and self-training using auxiliary information for out-of-distribution robustness","author":"Xie","year":"2021"},{"key":"10.1016\/j.inffus.2026.104336_bib0106","series-title":"Proceedings of the 61St ACM\/IEEE Design Automation Conference","article-title":"SMORE: Similarity-Based hyperdimensional domain adaptation for multi-Sensor time series classification","author":"Wang","year":"2024"},{"key":"10.1016\/j.inffus.2026.104336_bib0107","series-title":"Proceedings of the 37th International Conference on Neural Information Processing Systems","article-title":"Evolving standardization for continual domain generalization over temporal drift","author":"Xie","year":"2023"},{"key":"10.1016\/j.inffus.2026.104336_bib0108","series-title":"Proceedings of the 37th AAAI Conference on Artificial Intelligence","article-title":"Dish-TS: a general paradigm for alleviating distribution shift in time series forecasting","author":"Fan","year":"2023"},{"issue":"162","key":"10.1016\/j.inffus.2026.104336_bib0109","first-page":"1","article-title":"Conformal inference for online prediction with arbitrary distribution shifts","volume":"25","author":"Gibbs","year":"2024","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.inffus.2026.104336_bib0110","series-title":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","first-page":"3492","article-title":"Doubleadapt: a meta-learning approach to incremental learning for stock trend forecasting","author":"Zhao","year":"2023"},{"key":"10.1016\/j.inffus.2026.104336_bib0111","series-title":"Proceedings of the 14th International Conference on Learning Representations","article-title":"Online time series prediction using feature adjustment","author":"Huang","year":"2026"},{"key":"10.1016\/j.inffus.2026.104336_bib0112","series-title":"Proceedings of the 35th AAAI Conference on Artificial Intelligence","first-page":"9242","article-title":"Meta-Learning framework with applications to zero-Shot time-Series forecasting","volume":"35","author":"Oreshkin","year":"2021"},{"key":"10.1016\/j.inffus.2026.104336_bib0113","series-title":"Proceedings of the 19th IEEE\/CVF International Conference on Computer Vision","first-page":"5166","article-title":"Flatness-Aware minimization for domain generalization","author":"Zhang","year":"2023"},{"key":"10.1016\/j.inffus.2026.104336_bib0114","series-title":"Proceedings of the 12th 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.inffus.2026.104336_bib0115","series-title":"Proceedings of the 41St International Conference on Machine Learning","article-title":"Unified training of universal time series forecasting transformers","author":"Woo","year":"2024"},{"key":"10.1016\/j.inffus.2026.104336_bib0116","series-title":"Proceedings of the 39th AAAI Conference on Artificial Intelligence","article-title":"Chattime: a unified multimodal time series foundation model bridging numerical and textual data","author":"Wang","year":"2025"},{"key":"10.1016\/j.inffus.2026.104336_bib0117","series-title":"Proceedings of the 49th IEEE International Conference on Acoustics, Speech and Signal Processing","first-page":"8230","article-title":"ETP: Learning transferable ECG representations via ECG-Text pre-Training","author":"Liu","year":"2024"},{"key":"10.1016\/j.inffus.2026.104336_bib0118","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.inffus.2026.104336_bib0119","series-title":"Proceedings of the 39th AAAI Conference on Artificial Intelligence","article-title":"CALF: Aligning LLMs for time series forecasting via cross-modal fine-tuning","author":"Liu","year":"2025"},{"key":"10.1016\/j.inffus.2026.104336_bib0120","series-title":"Findings of the Association for Computational Linguistics: EACL 2023","first-page":"442","article-title":"Transfer knowledge from natural language to electrocardiography: can we detect cardiovascular disease through language models?","author":"Qiu","year":"2023"},{"key":"10.1016\/j.inffus.2026.104336_bib0121","series-title":"NeurIPS Workshop on Time Series in the Age of Large Models","article-title":"Align and fine-Tune: enhancing LLMs for time-Series forecasting","author":"Chang","year":"2024"},{"key":"10.1016\/j.inffus.2026.104336_bib0122","series-title":"Proceedings of the 14th International Conference on Learning Representations","article-title":"Timeomni-1: incentivizing complex reasoning with time series in large language models","author":"Guan","year":"2026"},{"key":"10.1016\/j.inffus.2026.104336_bib0123","series-title":"Proceedings of the 37th International Conference on Neural Information Processing Systems","article-title":"Large language models are zero-shot time series forecasters","author":"Gruver","year":"2023"},{"key":"10.1016\/j.inffus.2026.104336_bib0124","unstructured":"G. Woo, C. Liu, A. Kumar, D. Sahoo, Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain, 2023. 10.48550\/arXiv.2310.05063."},{"key":"10.1016\/j.inffus.2026.104336_bib0125","series-title":"Proceedings of the 34th ACM International Conference on Information and Knowledge Management","article-title":"Tabletime: reformulating time series classification as training-Free table understanding with large language models","author":"Wang","year":"2025"},{"key":"10.1016\/j.inffus.2026.104336_bib0126","series-title":"Proceedings of the 11th 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.inffus.2026.104336_bib0127","series-title":"Proceedings of the 37th International Conference on Neural Information Processing Systems","article-title":"ForecastPFN: synthetically-trained zero-shot forecasting","author":"Dooley","year":"2023"},{"key":"10.1016\/j.inffus.2026.104336_bib0128","series-title":"Proceedings of the 1St Workshop on Deep Generative Models for Health at NeurIPS 20233","article-title":"JoLT: jointly learned representations of language and time-Series","author":"Cai","year":"2023"},{"key":"10.1016\/j.inffus.2026.104336_bib0129","series-title":"Proceedings of the 33Rd ACM International Conference on Information and Knowledge Management","first-page":"3757","article-title":"General time transformer: an encoder-only foundation model for zero-Shot multivariate time series forecasting","author":"Feng","year":"2024"},{"key":"10.1016\/j.inffus.2026.104336_bib0130","series-title":"Proceedings of the 41St International Conference on Machine Learning","article-title":"MOMENT: A family of open time-series foundation models","author":"Goswami","year":"2024"},{"key":"10.1016\/j.inffus.2026.104336_bib0131","series-title":"Proceedings of the 41St International Conference on Machine Learning","article-title":"Timer: generative pre-trained transformers are large time series models","author":"Liu","year":"2024"},{"key":"10.1016\/j.inffus.2026.104336_bib0132","series-title":"Proceedings of the 41St International Conference on Machine Learning","article-title":"A decoder-only foundation model for time-series forecasting","author":"Das","year":"2024"},{"key":"10.1016\/j.inffus.2026.104336_bib0133","article-title":"Chronos: learning the language of time series","author":"Ansari","year":"2024","journal-title":"Transactions on Machine Learning Research"},{"key":"10.1016\/j.inffus.2026.104336_bib0134","series-title":"Proceedings of the 38th International Conference on Neural Information Processing Systems","article-title":"Tiny time mixers (TTMs): fast pre-trained models for enhanced zero\/few-shot forecasting of multivariate time series","author":"Ekambaram","year":"2024"},{"key":"10.1016\/j.inffus.2026.104336_bib0135","series-title":"R0-Fomo","article-title":"Lag-llama: towards foundation models for time series forecasting","author":"Rasul","year":"2023"},{"issue":"9","key":"10.1016\/j.inffus.2026.104336_bib0136","doi-asserted-by":"crossref","first-page":"5654","DOI":"10.1109\/TKDE.2025.3579137","article-title":"TimeRAF: retrieval-Augmented foundation model for zero-Shot time series forecasting","volume":"37","author":"Zhang","year":"2025","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.inffus.2026.104336_bib0137","series-title":"Proceedings of the 56th IEEE International Conference on Systems, Man, and Cybernetics","first-page":"3293","article-title":"Feature importance identification for time series classifiers","author":"Meng","year":"2022"},{"issue":"4","key":"10.1016\/j.inffus.2026.104336_bib0138","doi-asserted-by":"crossref","DOI":"10.1145\/2523813","article-title":"A survey on concept drift adaptation","volume":"46","author":"Gama","year":"2014","journal-title":"ACM Comput. Surv."},{"key":"10.1016\/j.inffus.2026.104336_bib0139","series-title":"Proceedings of the 14th International Conference on Learning Representations","article-title":"Tackling time-Series forecasting generalization via mitigating concept drift","author":"Zhao","year":"2026"},{"issue":"1","key":"10.1016\/j.inffus.2026.104336_bib0140","article-title":"Conformal inference for online prediction with arbitrary distribution shifts","volume":"25","author":"Gibbs","year":"2024","journal-title":"The Journal of Machine Learning Research"},{"key":"10.1016\/j.inffus.2026.104336_bib0141","series-title":"Proceedings of the 32Nd ACM International Conference on Multimedia","first-page":"2613","article-title":"Spatio-temporal heterogeneous federated learning for time series classification with multi-view orthogonal training","author":"Wu","year":"2024"},{"key":"10.1016\/j.inffus.2026.104336_bib0142","series-title":"Proceedings of the 31St International Joint Conference on Neural Networks","first-page":"1","article-title":"Unsupervised energy-based out-of-distribution detection using stiefel-Restricted kernel machine","author":"Tonin","year":"2021"},{"key":"10.1016\/j.inffus.2026.104336_bib0143","series-title":"Proceedings of the 34th IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"12100","article-title":"Causal hidden markov model for time series disease forecasting","author":"Li","year":"2021"},{"issue":"6","key":"10.1016\/j.inffus.2026.104336_bib0144","doi-asserted-by":"crossref","first-page":"3312","DOI":"10.1109\/TKDE.2025.3548160","article-title":"Dual test-Time training for out-of-Distribution recommender system","volume":"37","author":"Yang","year":"2025","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.inffus.2026.104336_bib0145","series-title":"Proceedings of the 41St International Conference on Machine Learning","article-title":"Position: what can large language models tell us about time series analysis","author":"Jin","year":"2024"},{"key":"10.1016\/j.inffus.2026.104336_bib0146","series-title":"Proceedings of the 38th Annual Conference on Neural Information Processing Systems","article-title":"Are language models actually useful for time series forecasting?","author":"Tan","year":"2024"},{"key":"10.1016\/j.inffus.2026.104336_bib0147","doi-asserted-by":"crossref","DOI":"10.1007\/s11263-026-02759-6","article-title":"A closer look at conditional prompt tuning for vision-Language models","author":"Zhang","year":"2026","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.inffus.2026.104336_bib0148","series-title":"Proceedings of the 34th IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops","first-page":"46","article-title":"Out-of-distribution detection and generation using soft brownian offset sampling and autoencoders","author":"M\u00f6ller","year":"2021"},{"issue":"6","key":"10.1016\/j.inffus.2026.104336_bib0149","doi-asserted-by":"crossref","DOI":"10.1007\/s11432-024-4333-7","article-title":"DiagLLM: multimodal reasoning with large language model for explainable bearing fault diagnosis","volume":"68","author":"Wang","year":"2025","journal-title":"Science China Information Sciences"},{"key":"10.1016\/j.inffus.2026.104336_bib0150","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.isprsjprs.2022.12.021","article-title":"Perception and sensing for autonomous vehicles under adverse weather conditions: a survey","volume":"196","author":"Zhang","year":"2023","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"10.1016\/j.inffus.2026.104336_bib0151","series-title":"Proceedings of the 41St International Conference on Machine Learning","article-title":"Out-of-domain generalization in dynamical systems reconstruction","author":"G\u00f6ring","year":"2024"},{"key":"10.1016\/j.inffus.2026.104336_bib0152","first-page":"1","article-title":"A new approach to interoperability within the smart city based on time series-Embedded adaptive traffic prediction modelling","author":"Fernandez","year":"2024","journal-title":"Netw. Spatial Econom."},{"issue":"12","key":"10.1016\/j.inffus.2026.104336_bib0153","doi-asserted-by":"crossref","first-page":"7805","DOI":"10.1109\/TKDE.2024.3447123","article-title":"Continual learning for smart city: a survey","volume":"36","author":"Yang","year":"2024","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"6","key":"10.1016\/j.inffus.2026.104336_bib0154","doi-asserted-by":"crossref","first-page":"2412","DOI":"10.1109\/TKDE.2019.2954510","article-title":"Deep air quality forecasting using hybrid deep learning framework","volume":"33","author":"Du","year":"2021","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.inffus.2026.104336_bib0155","doi-asserted-by":"crossref","DOI":"10.1016\/j.rse.2023.113609","article-title":"Predicting air quality via multimodal AI and satellite imagery","volume":"293","author":"Rowley","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.inffus.2026.104336_bib0156","doi-asserted-by":"crossref","DOI":"10.34133\/remotesensing.0285","article-title":"Remote sensing time series analysis: a review of data and applications","volume":"4","author":"Fu","year":"2024","journal-title":"J. Remote Sens."},{"issue":"7","key":"10.1016\/j.inffus.2026.104336_bib0157","doi-asserted-by":"crossref","first-page":"3529","DOI":"10.1109\/JBHI.2022.3157877","article-title":"A multimodal AI system for out-of-Distribution generalization of seizure identification","volume":"26","author":"Yang","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"1","key":"10.1016\/j.inffus.2026.104336_bib0158","doi-asserted-by":"crossref","DOI":"10.1145\/3552434","article-title":"Domain generalization for activity recognition via adaptive feature fusion","volume":"14","author":"Qin","year":"2022","journal-title":"ACM Trans. Intell. Syst. Technol."},{"issue":"1","key":"10.1016\/j.inffus.2026.104336_bib0159","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1089\/big.2020.0159","article-title":"Deep learning for time series forecasting: a survey","volume":"9","author":"Torres","year":"2021","journal-title":"Big Data"},{"key":"10.1016\/j.inffus.2026.104336_bib0160","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2022.108648","article-title":"Out-of-distribution detection-assisted trustworthy machinery fault diagnosis approach with uncertainty-aware deep ensembles","volume":"226","author":"Han","year":"2022","journal-title":"Reliab. Eng. Syst. Saf."},{"issue":"6","key":"10.1016\/j.inffus.2026.104336_bib0161","doi-asserted-by":"crossref","DOI":"10.1002\/widm.1216","article-title":"Data mining in distributed environment: a survey","volume":"7","author":"Gan","year":"2017","journal-title":"Wiley Interdisc. Rev. Data Min. Knowl. Discov."},{"issue":"2","key":"10.1016\/j.inffus.2026.104336_bib0162","doi-asserted-by":"crossref","DOI":"10.1145\/3704922","article-title":"Concept drift adaptation in text stream mining settings: a systematic review","volume":"16","author":"Garcia","year":"2025","journal-title":"ACM Trans. Intell. Syst. Technol."},{"issue":"7","key":"10.1016\/j.inffus.2026.104336_bib0163","first-page":"3366","article-title":"A continual learning survey: defying forgetting in classification tasks","volume":"44","author":"De Lange","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.inffus.2026.104336_bib0164","series-title":"Proceedings of the 22Nd IEEE\/CVF Winter Conference on Applications of Computer Vision","first-page":"3036","article-title":"Rethinking video anomaly detection - A continual learning approach","author":"Doshi","year":"2022"},{"key":"10.1016\/j.inffus.2026.104336_bib0165","series-title":"Proceedings of the 40th International Conference on Machine Learning","article-title":"Learnability and algorithm for continual learning","author":"Kim","year":"2023"},{"key":"10.1016\/j.inffus.2026.104336_bib0166","series-title":"Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track: European Conference, Proceedings, Part VII","first-page":"3","article-title":"Continually learning out-of-Distribution spatiotemporal data for robust energy forecasting","author":"Prabowo","year":"2023"},{"key":"10.1016\/j.inffus.2026.104336_bib0167","series-title":"Computer Vision - ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23\u201327, 2022, Proceedings, Part XIII","first-page":"361","article-title":"Trust, but verify: using self-supervised probing to improve trustworthiness","author":"Deng","year":"2022"},{"issue":"10","key":"10.1016\/j.inffus.2026.104336_bib0168","doi-asserted-by":"crossref","first-page":"10339","DOI":"10.1109\/TKDE.2023.3268125","article-title":"STD: A seasonal-Trend-Dispersion decomposition of time series","volume":"35","author":"Dudek","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"9","key":"10.1016\/j.inffus.2026.104336_bib0169","doi-asserted-by":"crossref","first-page":"4147","DOI":"10.1109\/TKDE.2020.3035685","article-title":"Developing an unsupervised real-Time anomaly detection scheme for time series with multi-Seasonality","volume":"34","author":"Wu","year":"2022","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.inffus.2026.104336_bib0170","series-title":"Proceedings of the 14th International Conference on Information Science and Technology","first-page":"840","article-title":"Multi-Granularity feature fusion network for cross-Domain person re-Identification","author":"Hou","year":"2024"},{"key":"10.1016\/j.inffus.2026.104336_bib0171","series-title":"Proceedings of the 31St ACM International Conference on Multimedia","first-page":"8515","article-title":"Improving anomaly segmentation with multi-Granularity cross-Domain alignment","author":"Zhang","year":"2023"},{"key":"10.1016\/j.inffus.2026.104336_bib0172","series-title":"Proceedings of the 11th International Conference on Learning Representations","article-title":"Scaleformer: iterative multi-scale refining transformers for time series forecasting","author":"Shabani","year":"2023"},{"key":"10.1016\/j.inffus.2026.104336_bib0173","series-title":"Proceedings of the 38th Annual Conference on Neural Information Processing Systems","article-title":"UniTS: a unified multi-Task time series model","author":"Gao","year":"2024"},{"key":"10.1016\/j.inffus.2026.104336_bib0174","series-title":"Findings of the Association for Computational Linguistics: EMNLP 2024","first-page":"3512","article-title":"Language models still struggle to zero-shot reason about time series","author":"Merrill","year":"2024"},{"issue":"9","key":"10.1016\/j.inffus.2026.104336_bib0175","doi-asserted-by":"crossref","first-page":"5311","DOI":"10.1109\/TKDE.2025.3527978","article-title":"How to bridge the gap between modalities: survey on multimodal large language model","volume":"37","author":"Song","year":"2025","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"10","key":"10.1016\/j.inffus.2026.104336_bib0176","doi-asserted-by":"crossref","first-page":"6469","DOI":"10.1109\/TSMC.2024.3427345","article-title":"Uncertainty-Aware fault diagnosis under calibration","volume":"54","author":"Lin","year":"2024","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems"},{"issue":"5","key":"10.1016\/j.inffus.2026.104336_bib0177","doi-asserted-by":"crossref","first-page":"3676","DOI":"10.1109\/TII.2025.3529920","article-title":"Uncertainty quantification based on conformal prediction for industrial time series with distribution shift","volume":"21","author":"Zhang","year":"2025","journal-title":"IEEE Trans. Ind. Inf."},{"issue":"C","key":"10.1016\/j.inffus.2026.104336_bib0178","article-title":"Data management for production quality deep learning models: challenges and solutions","volume":"191","author":"Munappy","year":"2022","journal-title":"J. Syst. Softw."},{"issue":"11","key":"10.1016\/j.inffus.2026.104336_bib0179","doi-asserted-by":"crossref","first-page":"4793","DOI":"10.1109\/TNNLS.2020.3027314","article-title":"A survey on explainable artificial intelligence (XAI): toward medical XAI","volume":"32","author":"Tjoa","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"C","key":"10.1016\/j.inffus.2026.104336_bib0180","article-title":"Explainable artificial intelligence: a survey of needs, techniques, applications, and future direction","volume":"599","author":"Mersha","year":"2024","journal-title":"Neurocomput."},{"issue":"5","key":"10.1016\/j.inffus.2026.104336_bib0181","doi-asserted-by":"crossref","first-page":"2571","DOI":"10.1007\/s10618-024-01041-y","article-title":"Explainable and interpretable machine learning and data mining","volume":"38","author":"Atzmueller","year":"2024","journal-title":"Data Min. Knowl. Discov."},{"issue":"1","key":"10.1016\/j.inffus.2026.104336_bib0182","article-title":"Recent emerging techniques in explainable artificial intelligence to enhance the interpretable and understanding of AI models for human","volume":"57","author":"Daniel","year":"2025","journal-title":"Neural Process. Lett."},{"issue":"1","key":"10.1016\/j.inffus.2026.104336_bib0183","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s12559-023-10179-8","article-title":"Interpreting black-Box models: a review on explainable artificial intelligence","volume":"16","author":"Vikas","year":"2024","journal-title":"Cognit. Comput."},{"issue":"5","key":"10.1016\/j.inffus.2026.104336_bib0184","doi-asserted-by":"crossref","DOI":"10.1145\/3711122","article-title":"Can graph neural networks be adequately explained? a survey","volume":"57","author":"Li","year":"2025","journal-title":"ACM Comput. Surv."},{"key":"10.1016\/j.inffus.2026.104336_bib0185","doi-asserted-by":"crossref","DOI":"10.1145\/3716317","article-title":"Navigating uncertainty: a user-Perspective survey of trustworthiness of AI in healthcare","author":"Ojha","year":"2025","journal-title":"ACM Trans. Comput. Healthce"},{"key":"10.1016\/j.inffus.2026.104336_bib0186","series-title":"Proceedings of the 14th International Conference on Learning Representations","article-title":"Mixlinear: extreme low-Resource multivariate time series forecasting with 0.1K parameters","author":"Ma","year":"2026"},{"key":"10.1016\/j.inffus.2026.104336_bib0187","series-title":"Proceedings of the 32Nd International Joint Conference on Artificial Intelligence","article-title":"Transformers in time series: a survey","author":"Wen","year":"2023"},{"key":"10.1016\/j.inffus.2026.104336_bib0188","series-title":"Proceedings of the 34th Annual Conference on Neural Information Processing Systems","first-page":"6441","article-title":"Benchmarking deep learning interpretability in time series predictions","volume":"33","author":"Ismail","year":"2020"},{"key":"10.1016\/j.inffus.2026.104336_bib0189","doi-asserted-by":"crossref","unstructured":"Y. Park, A. Tong, S. Lee, J. Seong, Q. Xie, J. Choi, Towards Transparent Time Series Analysis: Exploring Methods and Enhancing Interpretability, 2026. 10.1145\/3794839.","DOI":"10.1145\/3794839"},{"key":"10.1016\/j.inffus.2026.104336_bib0190","series-title":"Proceedings of the 35th Annual Conference on Neural Information Processing Systems","first-page":"6216","article-title":"Conformal time-series forecasting","volume":"34","author":"Stankeviciute","year":"2021"},{"key":"10.1016\/j.inffus.2026.104336_bib0191","series-title":"Proceedings of the 39th Annual Conference on Neural Information Processing Systems","article-title":"Conformal prediction for time-series forecasting with change points","author":"Sun","year":"2025"},{"issue":"10","key":"10.1016\/j.inffus.2026.104336_bib0192","doi-asserted-by":"crossref","first-page":"11575","DOI":"10.1109\/TPAMI.2023.3272339","article-title":"Conformal prediction for time series","volume":"45","author":"Xu","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Information Fusion"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1566253526002150?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1566253526002150?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T20:49:39Z","timestamp":1778618979000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1566253526002150"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":192,"alternative-id":["S1566253526002150"],"URL":"https:\/\/doi.org\/10.1016\/j.inffus.2026.104336","relation":{},"ISSN":["1566-2535"],"issn-type":[{"value":"1566-2535","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Out-of-Distribution Generalization in Time Series: A Survey","name":"articletitle","label":"Article Title"},{"value":"Information Fusion","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.inffus.2026.104336","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":"104336"}}