{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T16:05:50Z","timestamp":1770739550548,"version":"3.49.0"},"reference-count":70,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFB3107303"],"award-info":[{"award-number":["2023YFB3107303"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62172244"],"award-info":[{"award-number":["62172244"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Taishan Scholars Program","award":["tsqn202211210"],"award-info":[{"award-number":["tsqn202211210"]}]},{"name":"\u201c20 New Universities\u201d Project of Jinan City","award":["2021KJ001"],"award-info":[{"award-number":["2021KJ001"]}]},{"name":"\u201c20 New Universities\u201d Project of Jinan City","award":["202333023"],"award-info":[{"award-number":["202333023"]}]},{"name":"\u201c20 New Universities\u201d Project of Jinan City","award":["202333045"],"award-info":[{"award-number":["202333045"]}]},{"name":"Key Research and Development Program of Shandong","award":["ZR2024MF250"],"award-info":[{"award-number":["ZR2024MF250"]}]},{"name":"Key Research and Development Program of Shandong","award":["ZR2024MF250"],"award-info":[{"award-number":["ZR2024MF250"]}]},{"name":"Pairing Plan Project of the School of Computer Science and Technology of Qilu University of Technology","award":["2024JDJH12"],"award-info":[{"award-number":["2024JDJH12"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans.Inform.Forensic Secur."],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/tifs.2025.3639899","type":"journal-article","created":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T18:41:05Z","timestamp":1764787265000},"page":"13085-13100","source":"Crossref","is-referenced-by-count":0,"title":["DRCAD: Dual-View Experts Routing and Counterfactual Generation for Explainable Time Series Anomaly Detection"],"prefix":"10.1109","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1812-1316","authenticated-orcid":false,"given":"Dawei","family":"Zhao","sequence":"first","affiliation":[{"name":"Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5816-8385","authenticated-orcid":false,"given":"Haoran","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3386-4756","authenticated-orcid":false,"given":"Lijuan","family":"Xu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8182-2852","authenticated-orcid":false,"given":"Zhen","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Optics and Electronics (iOPEN), and the School of Mechanical Engineering, Northwestern Polytechnical University, Xi&#x2019;an, Shaanxi, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4415-0126","authenticated-orcid":false,"given":"Haipeng","family":"Peng","sequence":"additional","affiliation":[{"name":"Information Security Center, State Key Laboratory of Networking and Switching Technology, and the National Engineering Laboratory for Disaster Backup and Recovery, Beijing University of Posts and Telecommunications, Beijing, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/3464423"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2958185"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2024.3402439"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2021.3050605"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/1541880.1541882"},{"key":"ref6","first-page":"1","article-title":"Anomaly transformer: Time series anomaly detection with association discrepancy","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Xu"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.14778\/3538598.3538602"},{"key":"ref8","article-title":"Deep learning for anomaly detection: A survey","author":"Chalapathy","year":"2019","journal-title":"arXiv:1901.03407"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3691338"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599295"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2022.3188149"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2024.3380262"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE60146.2024.00099"},{"key":"ref14","first-page":"4765","article-title":"A unified approach to interpreting model predictions","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Lundberg"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N16-3020"},{"key":"ref16","first-page":"1488","article-title":"Countergan: Generating counterfactuals for real-time recourse and interpretability using residual gans","volume-title":"Proc. Uncertainty Artif. Intell.","author":"Nemirovsky"},{"key":"ref17","first-page":"841","article-title":"Counterfactual explanations without opening the black box: Automated decisions and the gdpr","volume":"31","author":"Wachter","year":"2018","journal-title":"Harv. JL Tech."},{"key":"ref18","first-page":"556","article-title":"Explaining a machine learning decision to physicians via counterfactuals","volume-title":"Proc. Conf. Health","author":"Nagesh"},{"key":"ref19","first-page":"2376","article-title":"Counterfactual visual explanations","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Goyal"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICAPAI49758.2021.9462056"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-86957-1_3"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1312.6114"},{"key":"ref23","first-page":"1","article-title":"Encoding time-series explanations through self-supervised model behavior consistency","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"36","author":"Queen"},{"key":"ref24","first-page":"799","article-title":"What went wrong and when? instance-wise feature importance for time-series black-box models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Tonekaboni"},{"key":"ref25","first-page":"1","article-title":"Temporal dependencies in feature importance for time series prediction","volume-title":"Proc. 11th Int. Conf. Learn. Represent.","author":"Leung"},{"key":"ref26","first-page":"39110","article-title":"Self-interpretable time series prediction with counterfactual explanations","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yan"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467075"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.111507"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.2307\/2284333"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-13-1642-5_33"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.105659"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2021.3087537"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.5555\/3524938.3525087"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01549"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.5555\/3495724.3497510"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"ref37","first-page":"1","article-title":"Moderntcn: A modern pure convolution structure for general time series analysis","volume-title":"Proc. 12th Int. Conf. Learn. Represent.","author":"Luo"},{"key":"ref38","first-page":"1","article-title":"CATCH: Channel-aware multivariate time series anomaly detection via frequency patching","volume-title":"Proc. 13th Int. Conf. Learn. Represent.","author":"Wu"},{"key":"ref39","first-page":"1","article-title":"A time series is worth 64 words: Long-term forecasting with transformers","volume-title":"Proc. 11th Int. Conf. Learn. Represent.","author":"Nie"},{"key":"ref40","first-page":"1","article-title":"An image is worth 16 \u00d7 16 words: Transformers for image recognition at scale","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Dosovitskiy"},{"key":"ref41","first-page":"3483","article-title":"Learning structured output representation using deep conditional generative models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"28","author":"Sohn"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467174"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219845"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CySWater.2016.7469060"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330672"},{"key":"ref46","first-page":"1","article-title":"Revisiting time series outlier detection: Definitions and benchmarks","volume-title":"Proc. 35th Conf. Neural Inf. Process. Syst. Datasets Benchmarks Track","author":"Lai"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2018.2801475"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/616"},{"key":"ref49","volume-title":"Time-Series","author":"Anderson","year":"1976"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330776"},{"key":"ref51","first-page":"43322","article-title":"One fits all: Power general time series analysis by pretrained lm","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"36","author":"Zhou"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1145\/342009.335388"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/TAES.2017.2671247"},{"key":"ref54","first-page":"1","article-title":"Deep autoencoding Gaussian mixture model for unsupervised anomaly detection","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Zong"},{"key":"ref55","first-page":"4393","article-title":"Deep one-class classification","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ruff"},{"key":"ref56","first-page":"13016","article-title":"Timeseries anomaly detection using temporal hierarchical one-class network","volume-title":"Proc. Conf. Neural Inf. Process. Syst. (NeurIPS)","volume":"33","author":"Shen"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412716"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2008.17"},{"key":"ref59","article-title":"Bayesian online changepoint detection","author":"Prescott Adams","year":"2007","journal-title":"arXiv:0710.3742"},{"key":"ref60","first-page":"4415","article-title":"U-time: A fully convolutional network for time series segmentation applied to sleep staging","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Perslev"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449903"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.5555\/2969033.2969125"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-86520-7_40"},{"key":"ref64","first-page":"8026","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Paszke"},{"key":"ref65","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014","journal-title":"arXiv:1412.6980"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372850"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.14778\/3551793.3551830"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref69","first-page":"16344","article-title":"FlashAttention: Fast and memory-efficient exact attention with IO-awareness","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Dao"},{"key":"ref70","article-title":"Generating long sequences with sparse transformers","author":"Child","year":"2019","journal-title":"arXiv:1904.10509"}],"container-title":["IEEE Transactions on Information Forensics and Security"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10206\/10810755\/11275873.pdf?arnumber=11275873","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T21:03:43Z","timestamp":1770671023000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11275873\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":70,"URL":"https:\/\/doi.org\/10.1109\/tifs.2025.3639899","relation":{},"ISSN":["1556-6013","1556-6021"],"issn-type":[{"value":"1556-6013","type":"print"},{"value":"1556-6021","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}