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The key problem is to improve the accuracy of model and capture the dynamic temporal and nonlinear spatial dependence. Using real data is one of the ways to improve the spatial\u2013temporal correlation modeling accuracy. However, real traffic flow data are not strictly periodic because of some random factors, which may lead to some deviations. This study focuses on capturing and modeling the temporal perturbation in real periodic data and we propose a spatial\u2013temporal similar graph attention network (STSGAN) to address this problem. In STSGAN, the spatial\u2013temporal graph convolution module is to capture local spatial\u2013temporal relationship in traffic data, and the periodic similar attention module is to treat the nonlinear traffic flow information. Experiments on three datasets demonstrate that our model is best among all methods. <\/jats:p>","DOI":"10.1142\/s0218126622501122","type":"journal-article","created":{"date-parts":[[2021,12,16]],"date-time":"2021-12-16T16:36:21Z","timestamp":1639672581000},"source":"Crossref","is-referenced-by-count":2,"title":["A Spatial\u2013Temporal Similar Graph Attention Network for Cyber Physical System Perception via Traffic Forecasting"],"prefix":"10.1142","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8081-4650","authenticated-orcid":false,"given":"Kaidi","family":"Zhao","sequence":"first","affiliation":[{"name":"Fudan University, Shanghai, P. R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingyue","family":"Xu","sequence":"additional","affiliation":[{"name":"Fudan University, Shanghai, P. R. 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