{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T10:31:06Z","timestamp":1750847466960,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031714696"},{"type":"electronic","value":"9783031714702"}],"license":[{"start":{"date-parts":[[2024,11,13]],"date-time":"2024-11-13T00:00:00Z","timestamp":1731456000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,13]],"date-time":"2024-11-13T00:00:00Z","timestamp":1731456000000},"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":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-71470-2_13","type":"book-chapter","created":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T18:02:20Z","timestamp":1731434540000},"page":"151-162","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Variational Autoencoder Based Automatic Clustering for\u00a0Multivariate Time Series Anomaly Detection"],"prefix":"10.1007","author":[{"given":"Li","family":"Yan","sequence":"first","affiliation":[]},{"given":"Hailin","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Kun","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Gaozhou","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Ti","family":"Guan","sequence":"additional","affiliation":[]},{"given":"Yanwei","family":"Zheng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,13]]},"reference":[{"issue":"2","key":"13_CR1","doi-asserted-by":"publisher","first-page":"1021","DOI":"10.1109\/TPWRD.2006.886797","volume":"22","author":"RA Leon","year":"2007","unstructured":"Leon, R.A., Vittal, V., Manimaran, G.: Application of sensor network for secure electric energy infrastructure. IEEE Trans. Power Delivery 22(2), 1021\u20131028 (2007)","journal-title":"IEEE Trans. Power Delivery"},{"key":"13_CR2","doi-asserted-by":"crossref","unstructured":"Zhang, W., Zhang, C., Tsung, F.: Grelen: multivariate time series anomaly detection from the perspective of graph relational learning. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 2390\u20132397","DOI":"10.24963\/ijcai.2022\/332"},{"key":"13_CR3","doi-asserted-by":"publisher","first-page":"1991","DOI":"10.1109\/ACCESS.2018.2886457","volume":"7","author":"M Munir","year":"2018","unstructured":"Munir, M., Siddiqui, S.A., Dengel, A., Ahmed, S.: Deepant: a deep learning approach for unsupervised anomaly detection in time series. IEEE Access 7, 1991\u20132005 (2018)","journal-title":"IEEE Access"},{"key":"13_CR4","doi-asserted-by":"crossref","unstructured":"Cheng, Y., Xu, Y., Zhong, H., Liu, Y.: Hs-tcn: a semi-supervised hierarchical stacking temporal convolutional network for anomaly detection in iot. In: 2019 IEEE 38th International Performance Computing and Communications Conference (IPCCC), pp.\u00a01\u20137. IEEE (2019)","DOI":"10.1109\/IPCCC47392.2019.8958755"},{"key":"13_CR5","doi-asserted-by":"crossref","unstructured":"Liu, J., Zhu, H., Liu, Y., Wu, H., Lan, Y., Zhang, X.: Anomaly detection for time series using temporal convolutional networks and gaussian mixture model. In: Journal of Physics: Conference Series. vol.\u00a01187, p. 042111. IOP Publishing (2019)","DOI":"10.1088\/1742-6596\/1187\/4\/042111"},{"key":"13_CR6","unstructured":"Malhotra, P., Ramakrishnan, A., Anand, G., Vig, L., Agarwal, P., Shroff, G.: Lstm-based encoder-decoder for multi-sensor anomaly detection. arXiv preprint arXiv:1607.00148 (2016)"},{"key":"13_CR7","doi-asserted-by":"crossref","unstructured":"Hundman, K., Constantinou, V., Laporte, C., Colwell, I., Soderstrom, T.: Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 387\u2013395 (2018)","DOI":"10.1145\/3219819.3219845"},{"key":"13_CR8","doi-asserted-by":"crossref","unstructured":"Su, Y., Zhao, Y., Niu, C., Liu, R., Sun, W., Pei, D.: Robust anomaly detection for multivariate time series through stochastic recurrent neural network. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2828\u20132837 (2019)","DOI":"10.1145\/3292500.3330672"},{"key":"13_CR9","unstructured":"Guo, Y., Liao, W., Wang, Q., Yu, L., Ji, T., Li, P.: Multidimensional time series anomaly detection: A gru-based gaussian mixture variational autoencoder approach. In: Asian Conference on Machine Learning, pp. 97\u2013112. PMLR (2018)"},{"key":"13_CR10","doi-asserted-by":"crossref","unstructured":"Zhao, H., et al.: Multivariate time-series anomaly detection via graph attention network. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 841\u2013850. IEEE (2020)","DOI":"10.1109\/ICDM50108.2020.00093"},{"issue":"12","key":"13_CR11","doi-asserted-by":"publisher","first-page":"9179","DOI":"10.1109\/JIOT.2021.3100509","volume":"9","author":"Z Chen","year":"2021","unstructured":"Chen, Z., Chen, D., Zhang, X., Yuan, Z., Cheng, X.: Learning graph structures with transformer for multivariate time-series anomaly detection in iot. IEEE Internet Things J. 9(12), 9179\u20139189 (2021)","journal-title":"IEEE Internet Things J."},{"key":"13_CR12","doi-asserted-by":"crossref","unstructured":"Park, D., Hoshi, Y., Kemp, C.C.: A multimodal anomaly detector for robot-assisted feeding using an lstm-based variational autoencoder. IEEE Robot. Automation Lett. 3(3), 1544\u20131551 (2018)","DOI":"10.1109\/LRA.2018.2801475"},{"key":"13_CR13","doi-asserted-by":"crossref","unstructured":"Deng, A., Hooi, B.: Graph neural network-based anomaly detection in multivariate time series. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a035, pp. 4027\u20134035 (2021)","DOI":"10.1609\/aaai.v35i5.16523"},{"key":"13_CR14","doi-asserted-by":"crossref","unstructured":"Han, S., Woo, S.S.: Learning sparse latent graph representations for anomaly detection in multivariate time series. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 2977\u20132986 (2022)","DOI":"10.1145\/3534678.3539117"},{"key":"13_CR15","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.aiopen.2022.07.001","volume":"3","author":"W Li","year":"2022","unstructured":"Li, W., Hu, W., Chen, T., Chen, N., Feng, C.: Stackvae-g: an efficient and interpretable model for time series anomaly detection. AI Open 3, 101\u2013110 (2022)","journal-title":"AI Open"},{"key":"13_CR16","doi-asserted-by":"crossref","unstructured":"Choi, K., Yi, J., Park, C., Yoon, S.: Deep learning for anomaly detection in time-series data: Review, analysis, and guidelines. IEEE Access PP(99), 1 (2021)","DOI":"10.1109\/ACCESS.2021.3107975"},{"key":"13_CR17","doi-asserted-by":"crossref","unstructured":"Kieu, T., Yang, B., Guo, C., Jensen, C.S.: Outlier detection for time series with recurrent autoencoder ensembles. In: IJCAI, pp. 2725\u20132732 (2019)","DOI":"10.24963\/ijcai.2019\/378"},{"key":"13_CR18","unstructured":"Malhotra, P., Vig, L., Shroff, G., Agarwal, P., et\u00a0al.: Long short term memory networks for anomaly detection in time series. In: Esann, vol.\u00a02015, p.\u00a089 (2015)"},{"key":"13_CR19","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)"},{"key":"13_CR20","doi-asserted-by":"crossref","unstructured":"Zhang, X., Li, W., Nguyen, V., Zhuang, F., Xiong, H., Lu, S.: Label-sensitive task grouping by bayesian nonparametric approach for multi-task multi-label learning. In: IJCAI, pp. 3125\u20133131 (2018)","DOI":"10.24963\/ijcai.2018\/434"},{"issue":"2","key":"13_CR21","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1023\/A:1007665907178","volume":"37","author":"MI Jordan","year":"1999","unstructured":"Jordan, M.I., Ghahramani, Z., Jaakkola, T.S., Saul, L.K.: An introduction to variational methods for graphical models. Mach. Learn. 37(2), 105\u2013161 (1999)","journal-title":"Mach. Learn."},{"issue":"1","key":"13_CR22","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1214\/06-BA104","volume":"1","author":"DM Blei","year":"2006","unstructured":"Blei, D.M., Jordan, M.I., et al.: Variational inference for Dirichlet process mixtures. Bayesian Anal. 1(1), 121\u2013143 (2006)","journal-title":"Bayesian Anal."},{"key":"13_CR23","unstructured":"Xue, Y., Liao, X., Carin, L., Krishnapuram, B.: Multi-task learning for classification with dirichlet process priors. JMLR 8(Jan), 35\u201363 (2007)"},{"key":"13_CR24","unstructured":"Bengio, S., Vinyals, O., Jaitly, N., Shazeer, N.: Scheduled sampling for sequence prediction with recurrent neural networks. Advances in neural information processing systems 28 (2015)"},{"key":"13_CR25","doi-asserted-by":"crossref","unstructured":"Goyal, P., Hu, Z., Liang, X., Wang, C., Xing, E.P.: Nonparametric variational auto-encoders for hierarchical representation learning. In: ICCV, pp. 5094\u20135102 (2017)","DOI":"10.1109\/ICCV.2017.545"}],"container-title":["Lecture Notes in Computer Science","Wireless Artificial Intelligent Computing Systems and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-71470-2_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T18:08:00Z","timestamp":1731434880000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-71470-2_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,13]]},"ISBN":["9783031714696","9783031714702"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-71470-2_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,11,13]]},"assertion":[{"value":"13 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"WASA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Wireless Artificial Intelligent Computing Systems and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Qingdao","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 June 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"wasa2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/wasa-conference.org\/WASA2024\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}