{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T11:26:23Z","timestamp":1764588383070},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T00:00:00Z","timestamp":1671408000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T00:00:00Z","timestamp":1671408000000},"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":["J Ambient Intell Human Comput"],"published-print":{"date-parts":[[2023,3]]},"DOI":"10.1007\/s12652-022-04493-6","type":"journal-article","created":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T07:06:59Z","timestamp":1671433619000},"page":"2399-2412","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Early warning model for industrial internet platform based on graph neural network and time convolution network"],"prefix":"10.1007","volume":"14","author":[{"given":"Chang","family":"Guo","sequence":"first","affiliation":[]},{"given":"Dechang","family":"Pi","sequence":"additional","affiliation":[]},{"given":"Jianjun","family":"Cao","sequence":"additional","affiliation":[]},{"given":"Xixuan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Hao","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,12,19]]},"reference":[{"key":"4493_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-020-02807-0","author":"J Canghong","year":"2021","unstructured":"Canghong J, Ruan T, Wu D et al (2021) HetGAT: a heterogeneous graph attention network for freeway traffic speed prediction. J Ambient Intell Humaniz Comput. https:\/\/doi.org\/10.1007\/s12652-020-02807-0","journal-title":"J Ambient Intell Humaniz Comput"},{"issue":"7","key":"4493_CR2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0041010","volume":"7","author":"V Dakos","year":"2012","unstructured":"Dakos V, Carpenter SR, Brock WA et al (2012) Methods for detecting early warnings ofcritical transitions in timeseries illustrated using simulated ecological data. PLoS ONE 7(7):e41010. https:\/\/doi.org\/10.1371\/journal.pone.0041010","journal-title":"PLoS ONE"},{"key":"4493_CR3","first-page":"703","volume-title":"28th International conference on artificial neural networks (ICANN)","author":"L Dan","year":"2019","unstructured":"Dan L, Chen D, Shi L et al (2019) MAD-GAN: Multivariate anomaly detection for time series data with generative adversarial networks. 28th International conference on artificial neural networks (ICANN). Springer, Berlin, pp 703\u2013716"},{"key":"4493_CR4","first-page":"3844","volume-title":"30th Conference on neural information processing systems (NIPS)","author":"M Defferrard","year":"2016","unstructured":"Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. 30th Conference on neural information processing systems (NIPS). MIT Press, Cambridge, pp 3844\u20133852"},{"key":"4493_CR5","first-page":"4027","volume-title":"35th AAAI conference on artificial intelligence","author":"A Deng","year":"2021","unstructured":"Deng A, Hooi B (2021) Graph neural network-based anomaly detection in multivariate time series. 35th AAAI conference on artificial intelligence. AAAI, Menlo Park, pp 4027\u20134035"},{"key":"4493_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2021.102839","author":"W Dongmin","year":"2022","unstructured":"Dongmin W, Deng Y, Li M (2022) FL-MGVN: federated learning for anomaly detection using mixed gaussian variational self-encoding network. Inf Process Manag. https:\/\/doi.org\/10.1016\/j.ipm.2021.102839","journal-title":"Inf Process Manag"},{"key":"4493_CR7","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-021-03685-w","author":"A Gupta","year":"2022","unstructured":"Gupta A, Nahar P (2022) Classification and yield prediction in smart agriculture system using IoT. J Ambient Intell Humaniz Comput. https:\/\/doi.org\/10.1007\/s12652-021-03685-w","journal-title":"J Ambient Intell Humaniz Comput"},{"issue":"3\u20134","key":"4493_CR8","doi-asserted-by":"publisher","first-page":"769","DOI":"10.1007\/s00704-017-2300-9","volume":"134","author":"O Hamidi","year":"2018","unstructured":"Hamidi O, Tapak L, Abbasi H et al (2018) Application of random forest time series, support vector regression and multivariate adaptive regression splines models in prediction of snowfall (a case study of Alvand in the middle Zagros, Iran). Theoret Appl Climatol 134(3\u20134):769\u2013776. https:\/\/doi.org\/10.1007\/s00704-017-2300-9","journal-title":"Theoret Appl Climatol"},{"key":"4493_CR9","doi-asserted-by":"publisher","first-page":"3009","DOI":"10.1145\/3292500.3330680","volume-title":"CM SIGKDD international conference on knowledge discovery and data mining (KDD)","author":"R Hansheng","year":"2019","unstructured":"Hansheng R, Xu B, Wang Y et al (2019) Time-series anomaly detection service at microsoft. CM SIGKDD international conference on knowledge discovery and data mining (KDD). ACM, New York, pp 3009\u20133017. https:\/\/doi.org\/10.1145\/3292500.3330680"},{"key":"4493_CR10","first-page":"187","volume-title":"World wide web (WWW) conference","author":"X Haowen","year":"2018","unstructured":"Haowen X, Chen W, Zhao N et al (2018) Unsupervised anomaly detection via variational auto-encoder for seasonal kpis in web applications. World wide web (WWW) conference. Springer, Berlin, pp 187\u2013196"},{"key":"4493_CR11","unstructured":"Kip FTN, Welling M (2019)Semi-supervised classification with graph convolutional networks. arXiv https:\/\/arxiv.org\/abs\/1609.02907"},{"key":"4493_CR12","first-page":"845","volume-title":"29th ACM international conference on information and knowledge management (CIKM)","author":"N Lim","year":"2020","unstructured":"Lim N, Hooi B, Ng S-K et al (2020) STP-UDGAT: spatial-temporal-preference user dimensional graph attention network for next poi recommendation. 29th ACM international conference on information and knowledge management (CIKM). ACM, New York, pp 845\u2013854"},{"issue":"3\u20134","key":"4493_CR13","doi-asserted-by":"publisher","first-page":"438","DOI":"10.1016\/j.jhydrol.2009.11.016","volume":"380","author":"C Lima","year":"2010","unstructured":"Lima C, Lall U (2010) Climate informed monthly streamflow forecasts for the Brazilian hydropower network using a periodic ridge regression model. J Hydrol 380(3\u20134):438\u2013449","journal-title":"J Hydrol"},{"issue":"3","key":"4493_CR14","doi-asserted-by":"publisher","first-page":"1383","DOI":"10.1007\/s12652-020-02636-1","volume":"13","author":"W Ming","year":"2022","unstructured":"Ming W, Jinfang Li, Kai W et al (2022) Anomaly detection for industrial control operations with optimized ABC-SVM and weighted function code correlation analysis. J Ambient Intell Humaniz Comput 13(3):1383\u20131396","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"4493_CR15","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1802.09089","author":"Y Mirsky","year":"2018","unstructured":"Mirsky Y, Doitshman T, Elovici Y et al (2018) Kitsune: an ensemble of autoencoders for online network intrusion detection. Annu Netw Distrib Syst Secur Symp (NDSS). https:\/\/doi.org\/10.48550\/arXiv.1802.09089","journal-title":"Annu Netw Distrib Syst Secur Symp (NDSS)"},{"key":"4493_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijinfomgt.2020.102282","author":"HD Nguyen","year":"2021","unstructured":"Nguyen HD, Tran KP, Thomassey S et al (2021) Forecasting and anomaly detection approaches using LSTM and LSTM autoencoder techniques with the applications in supply chain management. Int J Inf Manag. https:\/\/doi.org\/10.1016\/j.ijinfomgt.2020.102282","journal-title":"Int J Inf Manag"},{"issue":"10","key":"4493_CR17","doi-asserted-by":"publisher","first-page":"9449","DOI":"10.1007\/s12652-020-02685-6","volume":"12","author":"AH Rabie","year":"2020","unstructured":"Rabie AH, Saleh AI, Ali HA (2020) Smart electrical grids based on cloud, IoT, and big data technologies: state of the art. J Ambient Intell Humaniz Comput 12(10):9449\u20139480. https:\/\/doi.org\/10.1007\/s12652-020-02685-6","journal-title":"J Ambient Intell Humaniz Comput"},{"issue":"8","key":"4493_CR18","doi-asserted-by":"publisher","first-page":"876","DOI":"10.3390\/electronics8080876","volume":"8","author":"W Renzhuo","year":"2019","unstructured":"Renzhuo W, Shuping M, Jun W et al (2019) Multivariate temporal convolutional network:a deep neural networks approach for multivariate time series forecasting. Electronics 8(8):876\u2013885. https:\/\/doi.org\/10.3390\/electronics8080876","journal-title":"Electronics"},{"key":"4493_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2021.109885","author":"B Ruxue","year":"2021","unstructured":"Ruxue B, Xu Q, Zong M et al (2021) Rolling bearing fault diagnosis based on multi-channel convolution neural network and multi-scale clipping fusion data augmentation. Measurement. https:\/\/doi.org\/10.1016\/j.measurement.2021.109885","journal-title":"Measurement"},{"issue":"2","key":"4493_CR20","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1109\/MCI.2009.932254","volume":"4","author":"NL Sapankevych","year":"2009","unstructured":"Sapankevych NL, Sankar R (2009) Time series prediction using support vector machines: a survey. IEEE Comput Intell Mag 4(2):24\u201338. https:\/\/doi.org\/10.1109\/MCI.2009.932254","journal-title":"IEEE Comput Intell Mag"},{"key":"4493_CR21","unstructured":"Shaojie B, Kolter JZ, Koltun V (2018) An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv2018. https:\/\/arxiv.org\/pdf\/1803.01271.pdf"},{"key":"4493_CR22","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-021-03279-6","author":"SY Sheikh","year":"2021","unstructured":"Sheikh SY, Jilani MT (2021) A ubiquitous wheelchair fall detection system using low-cost embedded inertial sensors and unsupervised one-class SVM. J Ambient Intell Humaniz Comput. https:\/\/doi.org\/10.1007\/s12652-021-03279-6","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"4493_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2021.109929","author":"C Siya","year":"2021","unstructured":"Siya C, Jin G, Xinyu Ma (2021) Detection and analysis of real-time anomalies in large-scale complex system. Measurement. https:\/\/doi.org\/10.1016\/j.measurement.2021.109929","journal-title":"Measurement"},{"key":"4493_CR24","unstructured":"Tian Z, Ma Z, Wen Q et al (2022) FEDformer: frequency enhanced decomposed transformer for long-term series forecasting. In: Proceedings of the 39th International Conference on Machine Learning (ICML 2022). https:\/\/arxiv.org\/abs\/2201.12740"},{"key":"4493_CR25","unstructured":"Velickovi\u00b4c P, Cucurull G, Casanova A et al (2020)Graph attention networks. arXiv https:\/\/arxiv.org\/pdf\/1710.10903.pdf"},{"issue":"4","key":"4493_CR26","first-page":"3529","volume":"34","author":"C Weiqi","year":"2020","unstructured":"Weiqi C, Chen L, Xie Y et al (2020) Multi-range attentive bicomponent graph convolutional network for traffic forecasting. AAAI Conf Artif Intell 34(4):3529\u20133536","journal-title":"AAAI Conf Artif Intell"},{"issue":"5","key":"4493_CR27","doi-asserted-by":"publisher","first-page":"3365","DOI":"10.1007\/s00521-021-05712-5","volume":"34","author":"Y Xinzhe","year":"2022","unstructured":"Xinzhe Y, Jinghua Li, Shoujun H (2022) The improved genetic and BP hybrid algorithm and neural network economic early warning system. Neural Comput Appl 34(5):3365\u20133374. https:\/\/doi.org\/10.1007\/s00521-021-05712-5","journal-title":"Neural Comput Appl"},{"key":"4493_CR28","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330672","author":"S Ya","year":"2019","unstructured":"Ya S, Zhao Y, Niu C et al (2019) Robust anomaly detection for multivariate time series through stochastic recurrent neural network. ACM SIGKDD Int Conf Knowl Discov Data Min (KDD). https:\/\/doi.org\/10.1145\/3292500.3330672","journal-title":"ACM SIGKDD Int Conf Knowl Discov Data Min (KDD)"},{"issue":"8","key":"4493_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.patrec.2021.01.008","volume":"144","author":"C Yanyu","year":"2021","unstructured":"Yanyu C, Wenzhe Z, Wenbo Li et al (2021) Large group activity security risk assessment and risk early warning based on random forest algorithm. Pattern Recogn Lett 144(8):1\u20135. https:\/\/doi.org\/10.1016\/j.patrec.2021.01.008","journal-title":"Pattern Recogn Lett"},{"key":"4493_CR30","doi-asserted-by":"crossref","unstructured":"Zhao H, Wang Y, Duan J et al (2020) Multivariate time-series anomaly detection via graph attention network. In: 20th IEEE International Conference on Data Mining (ICDM), Piscataway, NJ, pp 841\u2013850 https:\/\/arxiv.org\/abs\/2009.02040","DOI":"10.1109\/ICDM50108.2020.00093"},{"key":"4493_CR31","unstructured":"Zong B, Qi S, Martin R et al (2018) Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In: International Conference on Learning Representations https:\/\/openreview.net\/pdf?id=BJJLHbb0-"}],"container-title":["Journal of Ambient Intelligence and Humanized Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12652-022-04493-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12652-022-04493-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12652-022-04493-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,2]],"date-time":"2023-03-02T16:15:19Z","timestamp":1677773719000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12652-022-04493-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,19]]},"references-count":31,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,3]]}},"alternative-id":["4493"],"URL":"https:\/\/doi.org\/10.1007\/s12652-022-04493-6","relation":{},"ISSN":["1868-5137","1868-5145"],"issn-type":[{"value":"1868-5137","type":"print"},{"value":"1868-5145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,19]]},"assertion":[{"value":"12 February 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 November 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 December 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}