{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T01:05:35Z","timestamp":1773795935745,"version":"3.50.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2025,3,17]],"date-time":"2025-03-17T00:00:00Z","timestamp":1742169600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,3,17]],"date-time":"2025-03-17T00:00:00Z","timestamp":1742169600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Applied Basic Research Project of Liaoning Province of China","award":["2023JH2\/101300183"],"award-info":[{"award-number":["2023JH2\/101300183"]}]},{"name":"Technology Plan Major Project of Liaoning Province of China","award":["2022JH1\/10400009"],"award-info":[{"award-number":["2022JH1\/10400009"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U24A20277"],"award-info":[{"award-number":["U24A20277"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2025,5]]},"DOI":"10.1007\/s40747-025-01839-3","type":"journal-article","created":{"date-parts":[[2025,3,17]],"date-time":"2025-03-17T16:27:47Z","timestamp":1742228867000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Self-attention-based graph transformation learning for anomaly detection in multivariate time series"],"prefix":"10.1007","volume":"11","author":[{"given":"Qiushi","family":"Wang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0000-1410-7915","authenticated-orcid":false,"given":"Yueming","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Zhicheng","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Dong","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yunbin","family":"Ma","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,17]]},"reference":[{"key":"1839_CR1","doi-asserted-by":"crossref","unstructured":"Choi K, Yi J, Park C, Yoon S (2021) Deep learning for anomaly detection in time-series data: Review, analysis, and guidelines. IEEE Access PP(99), 1\u20131","DOI":"10.1109\/ACCESS.2021.3107975"},{"key":"1839_CR2","unstructured":"Sengupta A, Steltzner A, Witkowski A, Rowan J. An Overview of the Mars Science Laboratory Parachute Decelerator System. IEEE"},{"key":"1839_CR3","unstructured":"Mathur AP, Tippenhauer NO. SWaT: A water treatment testbed for research and training on ICS Security. IEEE"},{"key":"1839_CR4","doi-asserted-by":"crossref","unstructured":"Song C, Liu S, Han G, Zeng P, Yu H, Zheng Q (2022) Edge-intelligence-based condition monitoring of beam pumping units under heavy noise in industrial internet of things for industry 4.0. IEEE Internet of Things J 10(4), 3037\u20133046","DOI":"10.1109\/JIOT.2022.3141382"},{"key":"1839_CR5","unstructured":"Malhotra P, Ramakrishnan A, Anand G, Vig L, Agarwal P, Shroff G (2016) Lstm-based encoder-decoder for multi-sensor anomaly detection. CoRR arXiv:1607.00148"},{"key":"1839_CR6","doi-asserted-by":"crossref","unstructured":"Liu S, Song C, Wu T, Zeng P (2023) A lightweight fault diagnosis method of beam pumping units based on dynamic warping matching and parallel deep network. IEEE Transactions on Systems, Man, and Cybernetics: Systems","DOI":"10.1109\/TSMC.2023.3328731"},{"key":"1839_CR7","doi-asserted-by":"crossref","unstructured":"Li Z, Zhao Y, Han J, Su Y, Jiao R, Wen X, Pei D (2021) Multivariate time series anomaly detection and interpretation using hierarchical inter-metric and temporal embedding. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 3220\u20133230","DOI":"10.1145\/3447548.3467075"},{"key":"1839_CR8","unstructured":"Vaswani A (2017) Attention is all you need. Advances in Neural Information Processing Systems"},{"key":"1839_CR9","unstructured":"Xu J, Wu H, Wang J, Long M (2022) Anomaly transformer: Time series anomaly detection with association discrepancy. In: International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=LzQQ89U1qm_"},{"issue":"11","key":"1839_CR10","doi-asserted-by":"publisher","first-page":"15964","DOI":"10.1109\/TITS.2024.3415435","volume":"25","author":"Z Zhang","year":"2024","unstructured":"Zhang Z, Yao Y, Hutabarat W, Farnsworth M, Tiwari D, Tiwari A (2024) Time series anomaly detection in vehicle sensors using self-attention mechanisms. IEEE Trans Intell Trans Syst 25(11):15964\u201315976. https:\/\/doi.org\/10.1109\/TITS.2024.3415435","journal-title":"IEEE Trans Intell Trans Syst"},{"key":"1839_CR11","unstructured":"Bai N, Wang X, Han R, Wang Q, Liu Z (2023) Paformer: Anomaly detection of time series with parallel-attention transformer. IEEE Transactions on Neural Networks and Learning Systems"},{"key":"1839_CR12","doi-asserted-by":"crossref","unstructured":"He Y, Zhao J (2019) Temporal convolutional networks for anomaly detection in time series. In: Journal of Physics: Conference Series, vol. 1213, p. 042050. IOP Publishing","DOI":"10.1088\/1742-6596\/1213\/4\/042050"},{"key":"1839_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107751","volume":"112","author":"M Thill","year":"2021","unstructured":"Thill M, Konen W, Wang H, B\u00e4ck T (2021) Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Appl Soft Comput 112:107751","journal-title":"Appl Soft Comput"},{"key":"1839_CR14","doi-asserted-by":"crossref","unstructured":"Deng A, Hooi B (2021) Graph neural network-based anomaly detection in multivariate time series. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 4027\u20134035","DOI":"10.1609\/aaai.v35i5.16523"},{"key":"1839_CR15","doi-asserted-by":"crossref","unstructured":"Zhang W, Zhang C, Tsung F (2022) Grelen: Multivariate time series anomaly detection from the perspective of graph relational learning. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, vol. 7, pp. 2390\u20132397","DOI":"10.24963\/ijcai.2022\/332"},{"key":"1839_CR16","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1016\/j.inffus.2022.08.011","volume":"89","author":"C Ding","year":"2023","unstructured":"Ding C, Sun S, Zhao J (2023) Mst-gat: A multimodal spatial-temporal graph attention network for time series anomaly detection. Inform Fus 89:527\u2013536","journal-title":"Inform Fus"},{"key":"1839_CR17","unstructured":"Hojjati H, Ho TKK, Armanfard N (2022) Self-supervised anomaly detection: a survey and outlook. arXiv preprint arXiv:2205.05173"},{"key":"1839_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110874","volume":"157","author":"ZZ Darban","year":"2025","unstructured":"Darban ZZ, Webb GI, Pan S, Aggarwal CC, Salehi M (2025) Carla: Self-supervised contrastive representation learning for time series anomaly detection. Pattern Recognit 157:110874","journal-title":"Pattern Recognit"},{"key":"1839_CR19","doi-asserted-by":"crossref","unstructured":"Yue Z, Wang Y, Duan J, Yang T, Huang C, Tong Y, Xu B (2022) Ts2vec: Towards universal representation of time series. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 8980\u20138987","DOI":"10.1609\/aaai.v36i8.20881"},{"key":"1839_CR20","doi-asserted-by":"crossref","unstructured":"Eldele E, Ragab M, Chen Z, Wu M, Kwoh CK, Li X, Guan C (2021) Time-series representation learning via temporal and contextual contrasting. In: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization","DOI":"10.24963\/ijcai.2021\/324"},{"key":"1839_CR21","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2017) Graph attention networks. arXiv preprint arXiv:1710.10903"},{"key":"1839_CR22","doi-asserted-by":"crossref","unstructured":"Jin M, Koh HY, Wen Q, Zambon D, Alippi C, Webb GI, King I, Pan S (2023) A survey on graph neural networks for time series: Forecasting, classification, imputation, and anomaly detection. arXiv preprint arXiv:2307.03759","DOI":"10.1109\/TPAMI.2024.3443141"},{"key":"1839_CR23","doi-asserted-by":"crossref","unstructured":"Breunig MM, Kriegel H-P, Ng RT, Sander J (2000) Lof: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp. 93\u2013104","DOI":"10.1145\/342009.335388"},{"key":"1839_CR24","doi-asserted-by":"crossref","unstructured":"Kiss I, Genge B, Haller P, Sebesty\u00e9n G (2014) Data clustering-based anomaly detection in industrial control systems. In: 2014 IEEE 10th International Conference on Intelligent Computer Communication and Processing (ICCP), pp. 275\u2013281. IEEE","DOI":"10.1109\/ICCP.2014.6937009"},{"key":"1839_CR25","doi-asserted-by":"crossref","unstructured":"Liu FT, Ting KM, Zhou Z-H (2008) Isolation forest. In: 2008 Eighth Ieee International Conference on Data Mining, pp. 413\u2013422. IEEE","DOI":"10.1109\/ICDM.2008.17"},{"key":"1839_CR26","unstructured":"Chalapathy R, Chawla S (2019) Deep learning for anomaly detection: A survey. arXiv preprint arXiv:1901.03407"},{"issue":"3","key":"1839_CR27","doi-asserted-by":"publisher","first-page":"1544","DOI":"10.1109\/LRA.2018.2801475","volume":"3","author":"D Park","year":"2018","unstructured":"Park D, Hoshi Y, Kemp CC (2018) A multimodal anomaly detector for robot-assisted feeding using an lstm-based variational autoencoder. IEEE Robot Auto Lett 3(3):1544\u20131551","journal-title":"IEEE Robot Auto Lett"},{"key":"1839_CR28","doi-asserted-by":"crossref","unstructured":"Su Y, Zhao Y, Niu C, Liu R, Sun W, Pei D (2019) Robust anomaly detection for multivariate time series through stochastic recurrent neural network. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2828\u20132837","DOI":"10.1145\/3292500.3330672"},{"key":"1839_CR29","doi-asserted-by":"crossref","unstructured":"Han S, Woo SS (2022) Learning sparse latent graph representations for anomaly detection in multivariate time series. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 2977\u20132986","DOI":"10.1145\/3534678.3539117"},{"key":"1839_CR30","unstructured":"Wang S, Zeng Y, Liu X, Zhu E, Yin J, Xu C, Kloft M (2019) Effective end-to-end unsupervised outlier detection via inlier priority of discriminative network. Advances in neural information processing systems 32"},{"key":"1839_CR31","unstructured":"Gidaris S, Singh P, Komodakis N (2018) Unsupervised representation learning by predicting image rotations. arXiv preprint arXiv:1803.07728"},{"key":"1839_CR32","doi-asserted-by":"crossref","unstructured":"Li C-L, Sohn K, Yoon J, Pfister T (2021) Cutpaste: Self-supervised learning for anomaly detection and localization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9664\u20139674","DOI":"10.1109\/CVPR46437.2021.00954"},{"key":"1839_CR33","unstructured":"Qiu C, Pfrommer T, Kloft M, Mandt S, Rudolph M (2021) Neural transformation learning for deep anomaly detection beyond images. In: International Conference on Machine Learning, pp. 8703\u20138714. PMLR"},{"key":"1839_CR34","unstructured":"Schneider T, Qiu C, Kloft M, Latif DA, Staab S, Mandt S, Rudolph M (2022) Detecting anomalies within time series using local neural transformations. arXiv preprint arXiv:2202.03944"},{"key":"1839_CR35","doi-asserted-by":"crossref","unstructured":"Qiu C, Kloft M, Mandt S, Rudolph M (2022) Raising the bar in graph-level anomaly detection. arXiv preprint arXiv:2205.13845","DOI":"10.24963\/ijcai.2022\/305"},{"key":"1839_CR36","first-page":"9881","volume":"35","author":"Y Liu","year":"2022","unstructured":"Liu Y, Wu H, Wang J, Long M (2022) Non-stationary transformers: exploring the stationarity in time series forecasting. Adv Neural Inform Process Syst 35:9881\u20139893","journal-title":"Adv Neural Inform Process Syst"},{"key":"1839_CR37","unstructured":"Ulyanov D, Vedaldi A, Lempitsky V (2016) Instance normalization: The missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022"},{"key":"1839_CR38","doi-asserted-by":"crossref","unstructured":"Noumir Z, Honeine P, Richard C (2012) On simple one-class classification methods. In: 2012 IEEE International Symposium on Information Theory Proceedings, pp. 2022\u20132026. IEEE","DOI":"10.1109\/ISIT.2012.6283685"},{"key":"1839_CR39","unstructured":"Ahmed CM, Palleti VR, Mathur AP. WADI: a Water Distribution Testbed for Research in the Design of Secure Cyber Physical Systems"},{"key":"1839_CR40","unstructured":"Berzal F (2014) Outlier analysis. Computing reviews (55-10)"},{"key":"1839_CR41","doi-asserted-by":"crossref","unstructured":"Li D, Chen D, Jin B, Shi L, Goh J, Ng S-K (2019) Mad-gan: Multivariate anomaly detection for time series data with generative adversarial networks. In: International Conference on Artificial Neural Networks, pp. 703\u2013716. Springer","DOI":"10.1007\/978-3-030-30490-4_56"},{"key":"1839_CR42","unstructured":"Wu H, Hu T, Liu Y, Zhou H, Wang J, Long M (2023) Timesnet: Temporal 2d-variation modeling for general time series analysis. In: International Conference on Learning Representations"},{"key":"1839_CR43","unstructured":"Dwivedi VP, Joshi CK, Luu AT, Laurent T, Bengio Y, Bresson X (2023) Benchmarking graph neural networks. Journal of Machine Learning Research 24"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-01839-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-025-01839-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-01839-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T10:36:15Z","timestamp":1745922975000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-025-01839-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,17]]},"references-count":43,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,5]]}},"alternative-id":["1839"],"URL":"https:\/\/doi.org\/10.1007\/s40747-025-01839-3","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,17]]},"assertion":[{"value":"15 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 February 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 March 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"214"}}