{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T18:45:36Z","timestamp":1784573136356,"version":"3.55.0"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T00:00:00Z","timestamp":1714089600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T00:00:00Z","timestamp":1714089600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci. China Inf. Sci."],"published-print":{"date-parts":[[2024,5]]},"DOI":"10.1007\/s11432-023-3899-1","type":"journal-article","created":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T15:01:57Z","timestamp":1714402917000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A credible traffic prediction method based on self-supervised causal discovery"],"prefix":"10.1007","volume":"67","author":[{"given":"Dan","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingjie","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,4,26]]},"reference":[{"key":"3899_CR1","doi-asserted-by":"publisher","first-page":"1160","DOI":"10.1109\/COMST.2021.3061981","volume":"23","author":"Y Siriwardhana","year":"2021","unstructured":"Siriwardhana Y, Porambage P, Liyanage M, et al. A survey on mobile augmented reality with 5G mobile edge computing: architectures, applications, and technical aspects. IEEE Commun Surv Tut, 2021, 23: 1160\u20131192","journal-title":"IEEE Commun Surv Tut"},{"key":"3899_CR2","doi-asserted-by":"publisher","first-page":"9517","DOI":"10.1109\/JIOT.2020.3003449","volume":"7","author":"J Du","year":"2020","unstructured":"Du J, Yu F R, Lu G, et al. MEC-assisted immersive VR video streaming over terahertz wireless networks: a deep reinforcement learning approach. IEEE Int Things J, 2020, 7: 9517\u20139529","journal-title":"IEEE Int Things J"},{"key":"3899_CR3","doi-asserted-by":"publisher","first-page":"773","DOI":"10.1007\/s10055-020-00492-0","volume":"25","author":"M J Liberatore","year":"2021","unstructured":"Liberatore M J, Wagner W P. Virtual, mixed, and augmented reality: a systematic review for immersive systems research. Virtual Reality, 2021, 25: 773\u2013799","journal-title":"Virtual Reality"},{"key":"3899_CR4","doi-asserted-by":"publisher","first-page":"3226","DOI":"10.1109\/JIOT.2021.3097754","volume":"9","author":"T Fang","year":"2022","unstructured":"Fang T, Yuan F, Ao L, et al. Joint task offloading, D2D pairing, and resource allocation in device-enhanced MEC: a potential game approach. IEEE Int Things J, 2022, 9: 3226\u20133237","journal-title":"IEEE Int Things J"},{"key":"3899_CR5","doi-asserted-by":"crossref","unstructured":"Portilla-Figueras A, Llopis-S\u00e1nchez S, Jim\u00e9nez-Fern\u00e1ndez S, et al. Examining 5G technology-based applications for military communications. In: Proceedings of the European Symposium on Research in Computer Security, 2022. 449\u2013465","DOI":"10.1007\/978-3-031-25460-4_26"},{"key":"3899_CR6","doi-asserted-by":"crossref","unstructured":"Chu P, Zhang J A, Wang X X, et al. Semi-persistent V2X resource allocation with traffic prediction in two-tier cellular networks. In: Proceedings of the 89th Vehicular Technology Conference (VTC2019-Spring), 2019. 1\u20136","DOI":"10.1109\/VTCSpring.2019.8746706"},{"key":"3899_CR7","doi-asserted-by":"publisher","first-page":"5549","DOI":"10.1007\/s00521-021-06708-x","volume":"34","author":"X Zhou","year":"2022","unstructured":"Zhou X, Zhang Y, Li Z, et al. Large-scale cellular traffic prediction based on graph convolutional networks with transfer learning. Neural Comput Appl, 2022, 34: 5549\u20135559","journal-title":"Neural Comput Appl"},{"key":"3899_CR8","doi-asserted-by":"crossref","unstructured":"Zheng T H, Li B C. Poisoning attacks on deep learning based wireless traffic prediction. In: Proceedings of the IEEE Conference on Computer Communications, 2022. 660\u2013669","DOI":"10.1109\/INFOCOM48880.2022.9796791"},{"key":"3899_CR9","doi-asserted-by":"publisher","first-page":"716","DOI":"10.1007\/s11036-019-01423-3","volume":"26","author":"Y Q Wang","year":"2021","unstructured":"Wang Y Q, Jiang D D, Huo L W, et al. A new traffic prediction algorithm to software defined networking. Mobile Netw Appl, 2021, 26: 716\u2013725","journal-title":"Mobile Netw Appl"},{"key":"3899_CR10","doi-asserted-by":"publisher","first-page":"2169","DOI":"10.1109\/TII.2020.3004232","volume":"17","author":"L S Nie","year":"2021","unstructured":"Nie L S, Ning Z L, Obaidat M S, et al. A reinforcement learning-based network traffic prediction mechanism in intelligent internet of things. IEEE Trans Ind Inf, 2021, 17: 2169\u20132180","journal-title":"IEEE Trans Ind Inf"},{"key":"3899_CR11","doi-asserted-by":"publisher","first-page":"102258","DOI":"10.1016\/j.adhoc.2020.102258","volume":"107","author":"M Li","year":"2020","unstructured":"Li M, Wang Y W, Wang Z W, et al. A deep learning method based on an attention mechanism for wireless network traffic prediction. Ad Hoc Netw, 2020, 107: 102258","journal-title":"Ad Hoc Netw"},{"key":"3899_CR12","unstructured":"Xu H Y, Huang Y D, Duan Z H, et al. Multivariate time series forecasting based on causal inference with transfer entropy and graph neural network. 2020. ArXiv:2005.01185"},{"key":"3899_CR13","unstructured":"Pearl J. Causal inference. In: Proceedings of Workshop on Causality: Objectives and Assessment at NIPS, 2010. 39\u201358"},{"key":"3899_CR14","doi-asserted-by":"crossref","unstructured":"Nogueira A R, Pugnana A, Ruggieri S, et al. Methods and tools for causal discovery and causal inference. WIREs Data Min Knowl, 2022, 12","DOI":"10.1002\/widm.1449"},{"key":"3899_CR15","doi-asserted-by":"publisher","first-page":"424","DOI":"10.2307\/1912791","volume":"37","author":"C W Granger","year":"1969","unstructured":"Granger C W. Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 1969, 37: 424\u2013438","journal-title":"Econometrica"},{"key":"3899_CR16","volume-title":"Time Series Analysis: Forecasting and Control","author":"G E Box","year":"2015","unstructured":"Box G E, Jenkins G M, Reinsel G C, et al. Time Series Analysis: Forecasting and Control. Hoboken: John Wiley & Sons, 2015"},{"key":"3899_CR17","first-page":"385","volume-title":"Modeling Financial Time Series with S-PLUS\u00ae","author":"E Zivot","year":"2006","unstructured":"Zivot E, Wang J H. Vector autoregressive models for multivariate time series. In: Modeling Financial Time Series with S-PLUS\u00ae. Berlin: Springer, 2006. 385\u2013429"},{"key":"3899_CR18","doi-asserted-by":"crossref","unstructured":"Wang J, Tang J, Xu Z Y, et al. Spatiotemporal modeling and prediction in cellular networks: a big data enabled deep learning approach. In: Proceedings of the IEEE Conference on Computer Communications, 2017. 1\u20139","DOI":"10.1109\/INFOCOM.2017.8057090"},{"key":"3899_CR19","doi-asserted-by":"crossref","unstructured":"Li Z Y, Fu Y C, Zhao P C, et al. A dynamic spatiotemporal prediction method for urban network traffic. In: Proceedings of the 96th Vehicular Technology Conference (VTC2022-Fall), 2022. 1\u20135","DOI":"10.1109\/VTC2022-Fall57202.2022.10012998"},{"key":"3899_CR20","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.neucom.2019.05.023","volume":"360","author":"K Wang","year":"2019","unstructured":"Wang K, Li K L, Zhou L Q, et al. Multiple convolutional neural networks for multivariate time series prediction. Neurocomputing, 2019, 360: 107\u2013119","journal-title":"Neurocomputing"},{"key":"3899_CR21","doi-asserted-by":"publisher","first-page":"1389","DOI":"10.1109\/JSAC.2019.2904363","volume":"37","author":"C T Zhang","year":"2019","unstructured":"Zhang C T, Zhang H X, Qiao J P, et al. Deep transfer learning for intelligent cellular traffic prediction based on cross-domain big data. IEEE J Sel Areas Commun, 2019, 37: 1389\u20131401","journal-title":"IEEE J Sel Areas Commun"},{"key":"3899_CR22","doi-asserted-by":"crossref","unstructured":"Zhang C T, Dang S P, Shihada B, et al. Dual attention-based federated learning for wireless traffic prediction. In: Proceedings of the IEEE Conference on Computer Communications, 2021. 1\u201310","DOI":"10.1109\/INFOCOM42981.2021.9488883"},{"key":"3899_CR23","doi-asserted-by":"publisher","first-page":"924","DOI":"10.1109\/TAI.2022.3150264","volume":"3","author":"L Cheng","year":"2022","unstructured":"Cheng L, Guo R C, Moraffah R, et al. Evaluation methods and measures for causal learning algorithms. IEEE Trans Artif Intell, 2022, 3: 924\u2013943","journal-title":"IEEE Trans Artif Intell"},{"key":"3899_CR24","doi-asserted-by":"publisher","first-page":"426","DOI":"10.1038\/s42256-020-0218-x","volume":"2","author":"Y N Luo","year":"2020","unstructured":"Luo Y N, Peng J, Ma J Z. When causal inference meets deep learning. Nat Mach Intell, 2020, 2: 426\u2013427","journal-title":"Nat Mach Intell"},{"key":"3899_CR25","doi-asserted-by":"publisher","first-page":"312","DOI":"10.3390\/make1010019","volume":"1","author":"M Nauta","year":"2019","unstructured":"Nauta M, Bucur D, Seifert C. Causal discovery with attention-based convolutional neural networks. MAKE, 2019, 1: 312\u2013340","journal-title":"MAKE"},{"key":"3899_CR26","unstructured":"L\u00f6we S, Madras D, Zemel R, et al. Amortized causal discovery: learning to infer causal graphs from time-series data. In: Proceedings of the Conference on Causal Learning and Reasoning, 2022. 509\u2013525"},{"key":"3899_CR27","doi-asserted-by":"publisher","first-page":"7235","DOI":"10.1109\/TNNLS.2021.3139389","volume":"34","author":"Y X Wang","year":"2023","unstructured":"Wang Y X, Cao F Y, Yu K, et al. Local causal discovery in multiple manipulated datasets. IEEE Trans Neural Netw Learn Syst, 2023, 34: 7235\u20137247","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"3899_CR28","unstructured":"Monti R P, Zhang K, Hyv\u00e4rinen A. Causal discovery with general non-linear relationships using non-linear ICA. In: Proceedings of the Uncertainty in Artificial Intelligence, 2020. 186\u2013195"},{"key":"3899_CR29","doi-asserted-by":"publisher","first-page":"3453","DOI":"10.1109\/TMC.2020.3001225","volume":"20","author":"L X Yu","year":"2021","unstructured":"Yu L X, Li M, Jin W Q, et al. STEP: a spatio-temporal fine-granular user traffic prediction system for cellular networks. IEEE Trans Mobile Comput, 2021, 20: 3453\u20133466","journal-title":"IEEE Trans Mobile Comput"},{"key":"3899_CR30","first-page":"4267","volume":"44","author":"A Tank","year":"2022","unstructured":"Tank A, Covert I, Foti N, et al. Neural Granger causality. IEEE Trans Pattern Anal Mach Intell, 2022, 44: 4267\u2013279","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3899_CR31","doi-asserted-by":"publisher","first-page":"150055","DOI":"10.1038\/sdata.2015.55","volume":"2","author":"G Barlacchi","year":"2015","unstructured":"Barlacchi G, De Nadai M, Larcher R, et al. A multi-source dataset of urban life in the city of Milan and the Province of Trentino. Sci Data, 2015, 2: 150055","journal-title":"Sci Data"},{"key":"3899_CR32","first-page":"1159","volume":"2","author":"H Y Sun","year":"2002","unstructured":"Sun H Y, Liu H X, Xiao H, et al. Short term traffic forecasting using the local linear regression model. SN Appl Sci, 2002, 2: 1159","journal-title":"SN Appl Sci"},{"key":"3899_CR33","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1109\/MCI.2009.932254","volume":"4","author":"N Sapankevych","year":"2009","unstructured":"Sapankevych N, Sankar R. Time series prediction using support vector machines: a survey. IEEE Comput Intell Mag, 2009, 4: 24\u201338","journal-title":"IEEE Comput Intell Mag"},{"key":"3899_CR34","doi-asserted-by":"publisher","first-page":"554","DOI":"10.1109\/LWC.2018.2795605","volume":"7","author":"C Qiu","year":"2018","unstructured":"Qiu C, Zhang Y Y, Feng Z Y, et al. Spatio-temporal wireless traffic prediction with recurrent neural network. IEEE Wireless Commun Lett, 2018, 7: 554\u2013557","journal-title":"IEEE Wireless Commun Lett"},{"key":"3899_CR35","doi-asserted-by":"crossref","unstructured":"Wang X, Zhao J, Zhu L, et al. Adaptive multi-receptive field spatial-temporal graph convolutional network for traffic forecasting. In: Proceedings of the IEEE Global Communications Conference (GLOBECOM), 2021. 1\u20137","DOI":"10.1109\/GLOBECOM46510.2021.9685054"},{"key":"3899_CR36","doi-asserted-by":"publisher","first-page":"2837","DOI":"10.1109\/TMC.2021.3129796","volume":"22","author":"Y Yao","year":"2023","unstructured":"Yao Y, Gu B, Su Z, et al. MVSTGN: a multi-view spatial-temporal graph network for cellular traffic prediction. IEEE Trans Mobile Comput, 2023, 22: 2837\u20132849","journal-title":"IEEE Trans Mobile Comput"}],"container-title":["Science China Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-023-3899-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11432-023-3899-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-023-3899-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T19:52:11Z","timestamp":1750362731000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11432-023-3899-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,26]]},"references-count":36,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,5]]}},"alternative-id":["3899"],"URL":"https:\/\/doi.org\/10.1007\/s11432-023-3899-1","relation":{},"ISSN":["1674-733X","1869-1919"],"issn-type":[{"value":"1674-733X","type":"print"},{"value":"1869-1919","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,26]]},"assertion":[{"value":"17 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 August 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 November 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 April 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"152303"}}