{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T08:28:44Z","timestamp":1768638524310,"version":"3.49.0"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2025,6,5]],"date-time":"2025-06-05T00:00:00Z","timestamp":1749081600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,6,5]],"date-time":"2025-06-05T00:00:00Z","timestamp":1749081600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["U1936213"],"award-info":[{"award-number":["U1936213"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Key research base of Humanities and Social Sciences in Guangdong Province\u2013Open Fund Project of Local Government Development Research Institute of Shantou University","award":["07422002"],"award-info":[{"award-number":["07422002"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"DOI":"10.1007\/s11227-025-07455-9","type":"journal-article","created":{"date-parts":[[2025,6,5]],"date-time":"2025-06-05T19:30:44Z","timestamp":1749151844000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Dynamic graph contrastive learning for multivariate time series anomaly detection"],"prefix":"10.1007","volume":"81","author":[{"given":"Anqin","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Pengzhou","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Yufei","family":"Gu","sequence":"additional","affiliation":[]},{"given":"Ting","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,5]]},"reference":[{"key":"7455_CR1","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1109\/RBME.2017.2757953","volume":"10","author":"S Ansari","year":"2017","unstructured":"Ansari S, Farzaneh N, Duda M, Horan K, Andersson HB, Goldberger ZD, Nallamothu BK, Najarian K (2017) A review of automated methods for detection of myocardial ischemia and infarction using electrocardiogram and electronic health records. IEEE Rev Biomed Eng 10:264\u2013298","journal-title":"IEEE Rev Biomed Eng"},{"key":"7455_CR2","doi-asserted-by":"crossref","unstructured":"Woike M, Abdul-Aziz A, Clem M (2014) Structural health monitoring on turbine engines using microwave blade tip clearance sensors. In: Smart sensor phenomena, technology, networks, and systems integration 2014, SPIE, vol. 9062, pp 167\u2013180","DOI":"10.1117\/12.2044967"},{"key":"7455_CR3","doi-asserted-by":"crossref","unstructured":"Cheng H, Tan P.-N, Potter C, Klooster S (2009) Detection and characterization of anomalies in multivariate time series. In: Proceedings of the 2009 SIAM International Conference on Data Mining, SIAM, pp 413\u2013424","DOI":"10.1137\/1.9781611972795.36"},{"key":"7455_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.109903","volume":"132","author":"C Sun","year":"2023","unstructured":"Sun C, He Z, Lin H, Cai L, Cai H, Gao M (2023) Anomaly detection of power battery pack using gated recurrent units based variational autoencoder. Appl Soft Comput 132:109903","journal-title":"Appl Soft Comput"},{"issue":"3","key":"7455_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3444690","volume":"54","author":"A Bl\u00e1zquez-Garc\u00eda","year":"2021","unstructured":"Bl\u00e1zquez-Garc\u00eda A, Conde A, Mori U, Lozano JA (2021) A review on outlier\/anomaly detection in time series data. ACM Comput Surv 54(3):1\u201333","journal-title":"ACM Comput Surv"},{"key":"7455_CR6","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.inffus.2022.10.008","volume":"91","author":"G Li","year":"2023","unstructured":"Li G, Jung JJ (2023) Deep learning for anomaly detection in multivariate time series: approaches, applications, and challenges. Inf Fusion 91:93\u2013102","journal-title":"Inf Fusion"},{"key":"7455_CR7","doi-asserted-by":"publisher","first-page":"10466","DOI":"10.1109\/TPAMI.2024.3443141","volume":"46","author":"M Jin","year":"2024","unstructured":"Jin M, Koh HY, Wen Q, Zambon D, Alippi C, Webb GI, King I, Pan S (2024) A survey on graph neural networks for time series: forecasting, classification, imputation, and anomaly detection. IEEE Trans Pattern Anal Mach Intell 46:10466\u201310485","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"7455_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.111002","volume":"280","author":"T Lei","year":"2023","unstructured":"Lei T, Gong C, Chen G, Ou M, Yang K, Li J (2023) A novel unsupervised framework for time series data anomaly detection via spectrum decomposition. Knowl-Based Syst 280:111002","journal-title":"Knowl-Based Syst"},{"key":"7455_CR9","doi-asserted-by":"crossref","unstructured":"Breunig M.M, Kriegel H.-P, Ng R.T, 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":"7455_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106186","volume":"204","author":"F Liu","year":"2020","unstructured":"Liu F, Yu Y, Song P, Fan Y, Tong X (2020) Scalable kde-based top-n local outlier detection over large-scale data streams. Knowl-Based Syst 204:106186","journal-title":"Knowl-Based Syst"},{"key":"7455_CR11","doi-asserted-by":"crossref","unstructured":"Yan Y, Cao L, Rundensteiner E.A (2017) Scalable top-n local outlier detection. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 1235\u20131244","DOI":"10.1145\/3097983.3098191"},{"issue":"7","key":"7455_CR12","doi-asserted-by":"publisher","first-page":"1443","DOI":"10.1162\/089976601750264965","volume":"13","author":"B Sch\u00f6lkopf","year":"2001","unstructured":"Sch\u00f6lkopf B, Platt JC, Shawe-Taylor J, Smola AJ, Williamson RC (2001) Estimating the support of a high-dimensional distribution. Neural Comput 13(7):1443\u20131471","journal-title":"Neural Comput"},{"key":"7455_CR13","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1023\/B:MACH.0000008084.60811.49","volume":"54","author":"DM Tax","year":"2004","unstructured":"Tax DM, Duin RP (2004) Support vector data description. Mach Learn 54:45\u201366","journal-title":"Mach Learn"},{"key":"7455_CR14","doi-asserted-by":"crossref","unstructured":"Hundman K, Constantinou V, Laporte C, Colwell I, Soderstrom T (2018) Detecting spacecraft anomalies using LSTMS and nonparametric dynamic thresholding. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 387\u2013395","DOI":"10.1145\/3219819.3219845"},{"key":"7455_CR15","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 and Data Mining, pp 2828\u20132837","DOI":"10.1145\/3292500.3330672"},{"key":"7455_CR16","unstructured":"Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. Advances in neural information processing systems 29"},{"key":"7455_CR17","first-page":"4027","volume":"35","author":"A Deng","year":"2021","unstructured":"Deng A, Hooi B (2021) Graph neural network-based anomaly detection in multivariate time series. Proc AAAI Conf Artif Intell 35:4027\u20134035","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"7455_CR18","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":"7455_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110725","volume":"275","author":"Y Shi","year":"2023","unstructured":"Shi Y, Wang B, Yu Y, Tang X, Huang C, Dong J (2023) Robust anomaly detection for multivariate time series through temporal GCNS and attention-based VAE. Knowl-Based Syst 275:110725","journal-title":"Knowl-Based Syst"},{"key":"7455_CR20","unstructured":"Kipf T.N, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907"},{"key":"7455_CR21","unstructured":"Xu J (2021) Anomaly transformer: time series anomaly detection with association discrepancy. arXiv preprint arXiv:2110.02642"},{"key":"7455_CR22","doi-asserted-by":"crossref","unstructured":"Yang Y, Zhang C, Zhou T, Wen Q, Sun L (2023) Dcdetector: dual attention contrastive representation learning for time series anomaly detection. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp 3033\u20133045","DOI":"10.1145\/3580305.3599295"},{"key":"7455_CR23","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1016\/j.ins.2022.07.179","volume":"610","author":"H Zhou","year":"2022","unstructured":"Zhou H, Yu K, Zhang X, Wu G, Yazidi A (2022) Contrastive autoencoder for anomaly detection in multivariate time series. Inf Sci 610:266\u2013280","journal-title":"Inf Sci"},{"key":"7455_CR24","first-page":"8481","volume":"38","author":"J He","year":"2024","unstructured":"He J, Xu Q, Jiang Y, Wang Z, Huang Q (2024) Ada-gad: anomaly-denoised autoencoders for graph anomaly detection. Proc AAAI Conf Artif Intell 38:8481\u20138489","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"7455_CR25","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 Autom Lett 3:1544\u20131551","journal-title":"IEEE Robot Autom Lett"},{"key":"7455_CR26","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, Springer, pp 703\u2013716","DOI":"10.1007\/978-3-030-30490-4_56"},{"key":"7455_CR27","doi-asserted-by":"crossref","unstructured":"Zhao H, Wang Y, Duan J, Huang C, Cao D, Tong Y, Xu B, Bai J, Tong J, Zhang Q (2020) Multivariate time-series anomaly detection via graph attention network. In: 2020 IEEE International Conference on Data Mining (ICDM), IEEE, pp 841\u2013850","DOI":"10.1109\/ICDM50108.2020.00093"},{"key":"7455_CR28","first-page":"1409","volume":"33","author":"C Zhang","year":"2019","unstructured":"Zhang C, Song D, Chen Y, Feng X, Lumezanu C, Cheng W, Ni J, Zong B, Chen H, Chawla NV (2019) A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data. Proc AAAI Conf Artif Intell 33:1409\u20131416","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"7455_CR29","unstructured":"Chen K, Feng M, Wirjanto T.S (2023) Multivariate time series anomaly detection via dynamic graph forecasting. arXiv preprint arXiv:2302.02051"},{"issue":"9","key":"7455_CR30","doi-asserted-by":"publisher","first-page":"11802","DOI":"10.1109\/TNNLS.2023.3325667","volume":"35","author":"Y Zheng","year":"2023","unstructured":"Zheng Y, Koh HY, Jin M, Chi L, Phan KT, Pan S, Chen Y-PP, Xiang W (2023) Correlation-aware spatial-temporal graph learning for multivariate time-series anomaly detection. IEEE Trans Neural Netw Learn Syst 35(9):11802\u201311816","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"7455_CR31","first-page":"11106","volume":"35","author":"H Zhou","year":"2021","unstructured":"Zhou H, Zhang S, Peng J, Zhang S, Li J, Xiong H, Zhang W (2021) Informer: beyond efficient transformer for long sequence time-series forecasting. Proc AAAI Conf Artif Intell 35:11106\u201311115","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"7455_CR32","unstructured":"Wu H, Hu T, Liu Y, Zhou H, Wang J, Long M (2022): Timesnet: temporal 2d-variation modeling for general time series analysis. arXiv preprint arXiv:2210.02186"},{"key":"7455_CR33","unstructured":"Chen P, Zhang Y, Cheng Y, Shu Y, Wang Y, Wen Q, Yang B, Guo C (2024) Pathformer: multi-scale transformers with adaptive pathways for time series forecasting. arXiv preprint arXiv:2402.05956"},{"key":"7455_CR34","unstructured":"Tang J, Li J, Gao Z, Li J (2022) Rethinking graph neural networks for anomaly detection. In: International Conference on Machine Learning, PMLR, pp 21076\u201321089"},{"key":"7455_CR35","doi-asserted-by":"crossref","unstructured":"Gao Y, Wang X, He X, Liu Z, Feng H, Zhang Y (2023) Addressing heterophily in graph anomaly detection: a perspective of graph spectrum. In: Proceedings of the ACM Web Conference 2023, pp 1528\u20131538","DOI":"10.1145\/3543507.3583268"},{"key":"7455_CR36","first-page":"11141","volume":"38","author":"W Cai","year":"2024","unstructured":"Cai W, Liang Y, Liu X, Feng J, Wu Y (2024) Msgnet: learning multi-scale inter-series correlations for multivariate time series forecasting. Proc AAAI Conf Artif Intell 38:11141\u201311149","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"7455_CR37","unstructured":"Vaswani A (2017) Attention is all you need. Advances in Neural Information Processing Systems"},{"key":"7455_CR38","first-page":"7194","volume":"36","author":"S Kim","year":"2022","unstructured":"Kim S, Choi K, Choi H-S, Lee B, Yoon S (2022) Towards a rigorous evaluation of time-series anomaly detection. Proc AAAI Conf Artif Intell 36:7194\u20137201","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"7455_CR39","doi-asserted-by":"publisher","first-page":"2774","DOI":"10.14778\/3551793.3551830","volume":"15","author":"J Paparrizos","year":"2022","unstructured":"Paparrizos J, Boniol P, Palpanas T, Tsay RS, Elmore A, Franklin MJ (2022) Volume under the surface: a new accuracy evaluation measure for time-series anomaly detection. Proc VLDB Endow 15:2774\u20132787","journal-title":"Proc VLDB Endow"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07455-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-025-07455-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07455-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,5]],"date-time":"2025-06-05T19:30:52Z","timestamp":1749151852000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-025-07455-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,5]]},"references-count":39,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["7455"],"URL":"https:\/\/doi.org\/10.1007\/s11227-025-07455-9","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,5]]},"assertion":[{"value":"16 May 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 June 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Anqin Zhang reports financial support was provided by National Natural Science Foundation of China. Anqin Zhang reports financial support was provided by Institute of local government development, Shantou University. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"974"}}