{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T08:44:37Z","timestamp":1782809077799,"version":"3.54.5"},"reference-count":40,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100005372","name":"Guilin University of Electronic Technology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005372","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Information Sciences"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.ins.2026.123829","type":"journal-article","created":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T16:15:14Z","timestamp":1782317714000},"page":"123829","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["STEN: A spatio-temporal enhancement network for anomaly detection in industrial multivariate time series"],"prefix":"10.1016","volume":"756","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0771-4992","authenticated-orcid":false,"given":"Yuan","family":"Xie","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baohua","family":"Qiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shihao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenhui","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruidong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lirui","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ins.2026.123829_bib0005","doi-asserted-by":"crossref","first-page":"1724","DOI":"10.1631\/jzus.A0820042","article-title":"Adaptive load forecasting of the hellenic electric grid","volume":"9","author":"Pappa","year":"2008","journal-title":"Zhejiang Univ. Sci. A"},{"key":"10.1016\/j.ins.2026.123829_bib0010","series-title":"Proc. IEEE Int. Joint Conf. Neural Netw","first-page":"1741","article-title":"Time-series novelty detection using one-class support vector machines","author":"Ma","year":"2003"},{"key":"10.1016\/j.ins.2026.123829_bib0015","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.1109\/TSMCA.2007.897589","article-title":"On the time series K-nearest neighbor classification of abnormal brain activity","volume":"37","author":"Chaovalitwongse","year":"2007","journal-title":"IEEE Trans. Syst. Man Cybern. A Syst. Humans"},{"key":"10.1016\/j.ins.2026.123829_bib0020","series-title":"Proc. AAAI Conf. Artif. Intell","first-page":"1409","article-title":"A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data","author":"Zhang","year":"2019"},{"key":"10.1016\/j.ins.2026.123829_bib0025","series-title":"Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min","first-page":"2828","article-title":"Robust anomaly detection for multivariate time series through stochastic recurrent neural network","author":"Su","year":"2019"},{"key":"10.1016\/j.ins.2026.123829_bib0030","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2025.132134","article-title":"MST-MFGAT: an adaptive temporal convolution with feature graph attention network for time series anomaly detection","volume":"664","author":"Wang","year":"2026","journal-title":"Neurocomputing"},{"key":"10.1016\/j.ins.2026.123829_bib0035","series-title":"Proc. Adv. Neural Inf. Process. Syst","first-page":"5998","article-title":"Attention is all you need","author":"Vaswani","year":"2017"},{"key":"10.1016\/j.ins.2026.123829_bib0040","series-title":"Proc. IEEE Int. Conf. Data Min","first-page":"841","article-title":"Multivariate time-series anomaly detection via graph attention network","author":"Zhao","year":"2020"},{"key":"10.1016\/j.ins.2026.123829_bib0045","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.111362","article-title":"Multi-step citywide traffic flow forecasting based on multiscale spatio-temporal transformer","volume":"158","author":"Zhang","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.ins.2026.123829_bib0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.111849","article-title":"DTAAD: dual TCN-attention networks for anomaly detection in multivariate time series data","volume":"295","author":"Yu","year":"2024","journal-title":"Knowl. Based Syst."},{"key":"10.1016\/j.ins.2026.123829_bib0055","series-title":"Proc. Int. Conf. Learn. Represent","article-title":"Deep autoencoding Gaussian mixture model for unsupervised anomaly detection","author":"Zong","year":"2018"},{"key":"10.1016\/j.ins.2026.123829_bib0060","series-title":"Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min","first-page":"3395","article-title":"USAD: unsupervised anomaly detection on multivariate time series","author":"Audibert","year":"2020"},{"key":"10.1016\/j.ins.2026.123829_bib0065","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1109\/TII.2021.3078414","article-title":"Adversarial autoencoder based feature learning for fault detection in industrial processes","volume":"18","author":"Jang","year":"2022","journal-title":"IEEE Trans. Ind. Inf."},{"key":"10.1016\/j.ins.2026.123829_bib0070","doi-asserted-by":"crossref","first-page":"1544","DOI":"10.1109\/LRA.2018.2801475","article-title":"A multimodal anomaly detector for robot-assisted feeding using an LSTM-based variational autoencoder","volume":"3","author":"Park","year":"2018","journal-title":"IEEE Robot. Autom. Lett."},{"key":"10.1016\/j.ins.2026.123829_bib0075","series-title":"Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min","first-page":"387","article-title":"Detecting spacecraft anomalies using LSTMs and nonparametric dynamic thresholding","author":"Hundman","year":"2018"},{"key":"10.1016\/j.ins.2026.123829_bib0080","series-title":"Proc. Int. Conf. Artif. Neural Netw","first-page":"703","article-title":"MAD-GAN: multivariate anomaly detection for time series data with generative adversarial networks","author":"Li","year":"2019"},{"key":"10.1016\/j.ins.2026.123829_bib0085","author":"Veli\u010dkovi\u0107"},{"key":"10.1016\/j.ins.2026.123829_bib0090","series-title":"Proc. AAAI Conf. Artif. Intell","first-page":"4027","article-title":"Graph neural network-based anomaly detection in multivariate time series","author":"Deng","year":"2021"},{"key":"10.1016\/j.ins.2026.123829_bib0095","article-title":"SSGC-GAT: synergistic similarity graph construction strategy combined with GAT network for wind turbine anomaly identification using SCADA data","volume":"73","author":"Wang","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.ins.2026.123829_bib0100","doi-asserted-by":"crossref","first-page":"3478","DOI":"10.1109\/TII.2023.3306935","article-title":"Compound-fault diagnosis of integrated energy systems based on graph embedded recurrent neural networks","volume":"20","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Ind. Inf."},{"key":"10.1016\/j.ins.2026.123829_bib0105","series-title":"Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min","first-page":"274","article-title":"MSHTrans: multi-scale hypergraph transformer with time-series decomposition for temporal anomaly detection","author":"Chen","year":"2025"},{"key":"10.1016\/j.ins.2026.123829_bib0110","series-title":"Proc. AAAI Conf. Artif. Intell","first-page":"11106","article-title":"Informer: beyond efficient transformer for long sequence time-series forecasting","author":"Zhou","year":"2021"},{"key":"10.1016\/j.ins.2026.123829_bib0115","doi-asserted-by":"crossref","first-page":"9179","DOI":"10.1109\/JIOT.2021.3100509","article-title":"Learning graph structures with transformer for multivariate time-series anomaly detection in IoT","volume":"9","author":"Chen","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.ins.2026.123829_bib0120","series-title":"Proc. Int. Conf. Mach. Learn","first-page":"27268","article-title":"FEDformer: frequency enhanced decomposed transformer for long-term series forecasting","author":"Zhou","year":"2022"},{"key":"10.1016\/j.ins.2026.123829_bib0125","series-title":"Proc. VLDB","first-page":"1201","article-title":"TranAD: deep transformer networks for anomaly detection in multivariate time series data","author":"Tuli","year":"2022"},{"key":"10.1016\/j.ins.2026.123829_bib0130","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2025.122279","article-title":"CT-DDPM: anomaly detection of multivariate time series with copula and transformer-based denoising diffusion probabilistic models","volume":"717","author":"Pan","year":"2025","journal-title":"Inf. Sci."},{"key":"10.1016\/j.ins.2026.123829_bib0135","series-title":"Proc. Int. Conf. Learn. Represent","article-title":"A time series is worth 64 words: long-term forecasting with transformers","author":"Nie","year":"2023"},{"key":"10.1016\/j.ins.2026.123829_bib0140","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.128960","article-title":"TCM: an efficient lightweight MLP-based network with affine transformation for long-term time series forecasting","volume":"617","author":"Jiang","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.ins.2026.123829_bib0145","series-title":"Proc. AAAI Conf. Artif. Intell","first-page":"11121","article-title":"Are transformers effective for time series forecasting?","author":"Zeng","year":"2023"},{"key":"10.1016\/j.ins.2026.123829_bib0150","doi-asserted-by":"crossref","first-page":"852","DOI":"10.1016\/j.ins.2021.08.042","article-title":"Exploiting dynamic spatio-temporal correlations for citywide traffic flow prediction using attention based neural networks","volume":"577","author":"Ali","year":"2021","journal-title":"Inf. Sci."},{"key":"10.1016\/j.ins.2026.123829_bib0155","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.neunet.2021.10.021","article-title":"Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction","volume":"145","author":"Ali","year":"2022","journal-title":"Neural Netw."},{"key":"10.1016\/j.ins.2026.123829_bib0160","series-title":"Proc. Int. Conf. Learn. Represent","article-title":"iTransformer: inverted transformers are effective for time series forecasting","author":"Liu","year":"2024"},{"key":"10.1016\/j.ins.2026.123829_bib0165","doi-asserted-by":"crossref","DOI":"10.1016\/j.chaos.2025.116898","article-title":"Dynamic multi-graph spatio-temporal learning for citywide traffic flow prediction in transportation systems","volume":"199","author":"Ali","year":"2025","journal-title":"Chaos Solit. Fractals"},{"key":"10.1016\/j.ins.2026.123829_bib0170","first-page":"2118","article-title":"Unsupervised deep anomaly detection for multi-sensor time-series signals","volume":"35","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.ins.2026.123829_bib0175","series-title":"Proc. CIKM","first-page":"1555","article-title":"Multivariate time-series anomaly detection based on enhancing graph attention networks with topological analysis","author":"Liu","year":"2024"},{"key":"10.1016\/j.ins.2026.123829_bib0180","doi-asserted-by":"crossref","first-page":"2471","DOI":"10.1109\/TII.2024.3507211","article-title":"Adversarial transformer-based anomaly detection for multivariate time series","volume":"21","author":"Yu","year":"2025","journal-title":"IEEE Trans. Ind. Inf."},{"key":"10.1016\/j.ins.2026.123829_bib0185","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2025.122790","article-title":"GDCMAD: graph-based dual-contrastive representation learning for multivariate time series anomaly detection","volume":"728","author":"He","year":"2026","journal-title":"Inf. Sci."},{"key":"10.1016\/j.ins.2026.123829_bib0190","series-title":"Proc. Int. Workshop Cyber-Phys. Syst. Smart Water Netw","first-page":"31","article-title":"SWaT: a water treatment testbed for research and training on ICS security","author":"Mathur","year":"2016"},{"key":"10.1016\/j.ins.2026.123829_bib0195","series-title":"Proc. Int. Workshop Cyber-Phys. Syst. Smart Water Netw","first-page":"25","article-title":"WADI: a water distribution testbed for research in the design of secure cyber physical systems","author":"Ahmed","year":"2017"},{"key":"10.1016\/j.ins.2026.123829_bib0200","series-title":"Proc. Adv. Neural Inf. Process. Syst","first-page":"16344","article-title":"Flashattention: fast and memory-efficient exact attention with IO-awareness","author":"Dao","year":"2022"}],"container-title":["Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0020025526007607?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0020025526007607?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T07:57:28Z","timestamp":1782806248000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0020025526007607"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":40,"alternative-id":["S0020025526007607"],"URL":"https:\/\/doi.org\/10.1016\/j.ins.2026.123829","relation":{},"ISSN":["0020-0255"],"issn-type":[{"value":"0020-0255","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"STEN: A spatio-temporal enhancement network for anomaly detection in industrial multivariate time series","name":"articletitle","label":"Article Title"},{"value":"Information Sciences","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ins.2026.123829","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"123829"}}