He, ShimingGuo, MengYang, BoAlfarraj, OsamaTolba, AmrSharma, Pradip KumarYan, Xi'ai2023-09-142023-09-142023-04-29He, S, Guo, M, Yang, B, Alfarraj, O, Tolba, A, Sharma, P K & Yan, X 2023, 'Fine-Grained Multivariate Time Series Anomaly Detection in IoT', Computers, Materials and Continua, vol. 75, no. 3, pp. 5027-5047. https://doi.org/10.32604/cmc.2023.0385511546-2218ORCID: /0000-0001-6620-9083/work/142570497https://hdl.handle.net/2164/21668Funding Information: Funding Statement: This work was supported in part by the National Natural Science Foundation of China under Grant 62272062, the Researchers Supporting Project number. (RSP2023R102) King Saud University, Riyadh, Saudi Arabia, the Open Research Fund of the Hunan Provincial Key Laboratory of Network Investigational Technology under Grant 2018WLZC003, the National Science Foundation of Hunan Province under Grant 2020JJ2029, the Hunan Provincial Key Research and Development Program under Grant 2022GK2019, the Science Fund for Creative Research Groups of Hunan Province under Grant 2020JJ1006, the Scientific Research Fund of Hunan Provincial Transportation Department under Grant 202143, and the Open Fund of Key Laboratory of Safety Control of Bridge Engineering, Ministry of Education (Changsha University of Science Technology) under Grant 21KB07. Publisher Copyright: © 2023 Tech Science Press. All rights reserved.211565762engfine-grained anomaly detectiongraph attention neural networkMultivariate time seriesQA75 Electronic computers. Computer scienceBiomaterialsModelling and SimulationMechanics of MaterialsComputer Science ApplicationsElectrical and Electronic EngineeringQA75Journal article10.32604/cmc.2023.038551http://www.scopus.com/inward/record.url?scp=85161339439&partnerID=8YFLogxK753