{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T10:58:16Z","timestamp":1743073096664,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819608393"},{"type":"electronic","value":"9789819608409"}],"license":[{"start":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T00:00:00Z","timestamp":1734048000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T00:00:00Z","timestamp":1734048000000},"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\/978-981-96-0840-9_26","type":"book-chapter","created":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T17:28:31Z","timestamp":1734024511000},"page":"372-385","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing Unsupervised Anomaly Detection in\u00a0Multivariate Time Series with\u00a0Variational Autoencoders and\u00a0Multiresolution LSTM"],"prefix":"10.1007","author":[{"given":"Song","family":"Sun","sequence":"first","affiliation":[]},{"given":"Yan","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Suyan","family":"Yao","sequence":"additional","affiliation":[]},{"given":"Jingbing","family":"Xu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,13]]},"reference":[{"issue":"4","key":"26_CR1","doi-asserted-by":"publisher","first-page":"2046","DOI":"10.1080\/03772063.2021.1886600","volume":"69","author":"SMAK Azad","year":"2023","unstructured":"Azad, S.M.A.K., Srinivasan, K.: Anomaly detection in estimation of load and prediction of load in networked control system using correlation and regression data analysis. IETE J. Res. 69(4), 2046\u20132056 (2023)","journal-title":"IETE J. Res."},{"issue":"12","key":"26_CR2","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":"26_CR3","unstructured":"Pankaj Malhotra, Anusha Ramakrishnan, Gaurangi Anand, Lovekesh Vig, Puneet Agarwal, and Gautam Shroff. Lstm-based encoder-decoder for multi-sensor anomaly detection. 07 2016"},{"issue":"3","key":"26_CR4","doi-asserted-by":"publisher","first-page":"1704","DOI":"10.1109\/TCYB.2019.2933548","volume":"51","author":"Y-H Yoo","year":"2021","unstructured":"Yoo, Y.-H., Kim, U.-H., Kim, J.-H.: Recurrent reconstructive network for sequential anomaly detection. IEEE Transactions on Cybernetics 51(3), 1704\u20131715 (2021)","journal-title":"IEEE Transactions on Cybernetics"},{"issue":"2","key":"26_CR5","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1162\/neco.1992.4.2.234","volume":"4","author":"J Schmidhuber","year":"1992","unstructured":"Schmidhuber, J.: Learning complex, extended sequences using the principle of history compression. Neural Comput. 4(2), 234\u2013242 (1992)","journal-title":"Neural Comput."},{"key":"26_CR6","unstructured":"Salah El\u00a0Hihi and Yoshua Bengio. Hierarchical recurrent neural networks for long-term dependencies. In Advances in neural information processing systems, pages 493\u2013499, 1996"},{"key":"26_CR7","unstructured":"Jan Koutn\u00edk, Klaus Greff, Faustino Gomez, and J\u00fcrgen Schmidhuber. A clockwork rnn. In Proceedings of the 31st International Conference on International Conference on Machine Learning, ICML\u201914, 2014"},{"key":"26_CR8","unstructured":"Junyoung Chung, Caglar Gulcehre, Kyunghyun Cho, and Yoshua Bengio. Gated feedback recurrent neural networks. In Proceedings of the 32nd International Conference on Machine Learning, volume\u00a037, pages 2067\u20132075, 2015"},{"key":"26_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.101819","volume":"97","author":"Z Chen","year":"2023","unstructured":"Chen, Z., Ma, M., Li, T., Wang, H., Li, C.: Long sequence time-series forecasting with deep learning: A survey. Information Fusion 97, 101819 (2023)","journal-title":"Information Fusion"},{"key":"26_CR10","unstructured":"Pankaj Malhotra, Lovekesh Vig, Gautam Shroff, and Puneet Agarwal. Long short term memory networks for anomaly detection in time series. 04 2015"},{"key":"26_CR11","unstructured":"Dan Li, Dacheng Chen, Jonathan Goh, and See-kiong Ng. Anomaly detection with generative adversarial networks for multivariate time series. In ACM Knowledge Discovery and Data Mining conference, 2018"},{"key":"26_CR12","doi-asserted-by":"crossref","unstructured":"Lukas Ruff, Jacob\u00a0R Kauffmann, Robert\u00a0A Vandermeulen, Gr\u00e9goire Montavon, Wojciech Samek, Marius Kloft, Thomas\u00a0G Dietterich, and Klaus-Robert M\u00fcller. A unifying review of deep and shallow anomaly detection. Proceedings of the IEEE, 109(5):756\u2013795, 2021","DOI":"10.1109\/JPROC.2021.3052449"},{"key":"26_CR13","doi-asserted-by":"crossref","unstructured":"Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th international conference on Machine learning (ICML), pages 1096\u20131103, 2008","DOI":"10.1145\/1390156.1390294"},{"key":"26_CR14","doi-asserted-by":"crossref","unstructured":"Tung Kieu, Bin Yang, Chenjuan Guo, and Christian\u00a0S. Jensen. Outlier detection for time series with recurrent autoencoder ensembles. In IJCAI-19, pages 2725\u20132732, 7 2019","DOI":"10.24963\/ijcai.2019\/378"},{"key":"26_CR15","unstructured":"Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, CVPR\u201916, pages 770\u2013778, 2016"},{"key":"26_CR16","doi-asserted-by":"publisher","first-page":"4027","DOI":"10.1609\/aaai.v35i5.16523","volume":"35","author":"A Deng","year":"2021","unstructured":"Deng, A., Hooi, B.: Graph neural network-based anomaly detection in multivariate time series. In Proceedings of the AAAI conference on artificial intelligence 35, 4027\u20134035 (2021)","journal-title":"In Proceedings of the AAAI conference on artificial intelligence"},{"key":"26_CR17","doi-asserted-by":"crossref","unstructured":"Jing Ren, Feng Xia, Ivan Lee, Azadeh Noori\u00a0Hoshyar, and Charu Aggarwal. Graph learning for anomaly analytics: Algorithms, applications, and challenges. ACM Transactions on Intelligent Systems and Technology, 14(2):1\u201329, 2023","DOI":"10.1145\/3570906"},{"key":"26_CR18","doi-asserted-by":"crossref","unstructured":"Aditya\u00a0P. Mathur and Nils\u00a0Ole Tippenhauer. Swat: a water treatment testbed for research and training on ics security. In 2016 International Workshop on Cyber-physical Systems for Smart Water Networks (CySWater), pages 31\u201336, 2016","DOI":"10.1109\/CySWater.2016.7469060"},{"key":"26_CR19","doi-asserted-by":"crossref","unstructured":"Kyle Hundman, Valentino Constantinou, Christopher Laporte, Ian Colwell, and Tom Soderstrom. Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD \u201918, page 387-395, 2018","DOI":"10.1145\/3219819.3219845"},{"key":"26_CR20","doi-asserted-by":"crossref","unstructured":"Viraaji Mothukuri, Reza\u00a0M Parizi, Seyedamin Pouriyeh, Yan Huang, Ali Dehghantanha, and Gautam Srivastava. A survey on security and privacy of federated learning. Future Generation Computer Systems, 115:619\u2013640, 2021","DOI":"10.1016\/j.future.2020.10.007"},{"key":"26_CR21","unstructured":"Bo\u00a0Zong, Qi\u00a0Song, Martin\u00a0Renqiang Min, Wei Cheng, Cristian Lumezanu, Daeki Cho, and Haifeng Chen. Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In International conference on learning representations, ICLR 2018, 2018"},{"issue":"3","key":"26_CR22","doi-asserted-by":"publisher","first-page":"1544","DOI":"10.1109\/LRA.2018.2801475","volume":"3","author":"D Park","year":"2018","unstructured":"Park, D., Hoshi, Y., Kemp, C.C.: A multimodal anomaly detector for robot-assisted feeding using an lstm-based variational autoencoder. IEEE Robotics and Automation Letters 3(3), 1544\u20131551 (2018)","journal-title":"IEEE Robotics and Automation Letters"},{"key":"26_CR23","doi-asserted-by":"crossref","unstructured":"Julien Audibert, Pietro Michiardi, Fr\u00e9d\u00e9ric Guyard, S\u00e9bastien Marti, and Maria\u00a0A. Zuluaga. Usad: Unsupervised anomaly detection on multivariate time series. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD \u201920, page 3395-3404, 2020","DOI":"10.1145\/3394486.3403392"}],"container-title":["Lecture Notes in Computer Science","Advanced Data Mining and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-0840-9_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T18:10:02Z","timestamp":1734027002000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-0840-9_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,13]]},"ISBN":["9789819608393","9789819608409"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-0840-9_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,12,13]]},"assertion":[{"value":"13 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declaration of Competing Interest"}},{"value":"ADMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Data Mining and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sydney, NSW","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","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":"3 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adma2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adma2024.github.io\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}