{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T06:38:08Z","timestamp":1774507088572,"version":"3.50.1"},"reference-count":28,"publisher":"IEEE","license":[{"start":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T00:00:00Z","timestamp":1593561600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T00:00:00Z","timestamp":1593561600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T00:00:00Z","timestamp":1593561600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7]]},"DOI":"10.1109\/ijcnn48605.2020.9207013","type":"proceedings-article","created":{"date-parts":[[2020,9,30]],"date-time":"2020-09-30T00:40:33Z","timestamp":1601426433000},"page":"1-7","source":"Crossref","is-referenced-by-count":27,"title":["Hybrid approach for Anomaly Detection in Time Series Data"],"prefix":"10.1109","author":[{"given":"Zeineb","family":"Ghrib","sequence":"first","affiliation":[]},{"given":"Rakia","family":"Jaziri","sequence":"additional","affiliation":[]},{"given":"Rim","family":"Romdhane","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","first-page":"37","article-title":"Autoencoders, unsupervised learning, and deep architectures","volume":"27","author":"baldi","year":"2012","journal-title":"JMLR Workshop and Conference Proceedings"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/8676387"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.promfg.2018.10.023"},{"key":"ref13","article-title":"Generative adversarial nets","author":"ian","year":"2014"},{"key":"ref14","article-title":"Application of generative autoencoder in de novo molecular design","author":"blaschke","year":"2017","journal-title":"Journal of Cheminformatics"},{"key":"ref15","article-title":"A survey of anomaly detection techniques in financial domain","author":"ahmed","year":"2015"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2015.10.026"},{"key":"ref17","article-title":"Support vector method for novelty detection","author":"platt","year":"1999","journal-title":"NIPS&#x2019;99 Proceedings of the 12th International Conference on Neural Information Processing Systems"},{"key":"ref18","article-title":"Anomaly detection using autoencoders in high performance computer systems","author":"andrea borghesi","year":"2018"},{"key":"ref19","article-title":"Lstm-based encoder-decoder for multisensor anomaly detection","author":"malhotra","year":"2016","journal-title":"TCS Research"},{"key":"ref28","article-title":"Random search for hyper-parameter optimization","author":"james bergstra","year":"2012","journal-title":"Journal of Machine Learning Research"},{"key":"ref4","article-title":"Long short term memory networks for anomaly detection in time series","author":"malhotra","year":"2015","journal-title":"European Symposium on Artificial Neural Networks Computational Intelligence and Machine Learning"},{"key":"ref27","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-70139-4","article-title":"Lightgbm: A highly efficient gradient boosting decision tree","author":"liu","year":"2017","journal-title":"Advances in Neural Information Processing Systems 30 (NIP 2017)"},{"key":"ref3","article-title":"Isolation forest","author":"zhou","year":"2008","journal-title":"Eighth IEEE International Conference on Data Mining"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref5","article-title":"Time series feature extraction","author":"leonard","year":"2018"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298935"},{"key":"ref7","article-title":"Sequence to sequence learning with neural networks","author":"ilya sutskever","year":"2014","journal-title":"Advances in neural information processing systems"},{"key":"ref2","first-page":"93","article-title":"Lof: Identifying density-based local outliers, in acm sigmod record","volume":"29","author":"markus","year":"2000","journal-title":"Procedia Manufacturing"},{"key":"ref9","article-title":"Generating sequences with recurrent neural network","author":"alex","year":"2014"},{"key":"ref1","doi-asserted-by":"crossref","first-page":"1466","DOI":"10.1016\/j.neucom.2006.05.013","article-title":"One-class document classification via neural networks","volume":"70","author":"larry","year":"2007","journal-title":"Neurocomputing"},{"key":"ref20","article-title":"Anomaly detection using one-class neural networks","author":"chalapathy","year":"2019"},{"key":"ref22","first-page":"843","article-title":"Unsupervised learning of video representations using lstms","volume":"37","author":"nitish srivastava","year":"2015","journal-title":"ICML&#x2019;15 Proceedings of the 32nd International Conference on International Conference on Machine Learning"},{"key":"ref21","first-page":"15","article-title":"Learning long-term dependencies is difficult","volume":"5","author":"bengio","year":"1997","journal-title":"IEEE"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/DEXA.1999.795279"},{"key":"ref23","first-page":"1724","article-title":"Learning phrase representations using rnn encoder&#x2013;decoder for statistical machine translation","author":"cho","year":"1997","journal-title":"Association for Computational Linguistics"},{"key":"ref26","article-title":"Using random forest to learn imbalanced data","author":"chao chen","year":"2004"},{"key":"ref25","volume":"45","author":"breiman","year":"2001","journal-title":"Random Forests"}],"event":{"name":"2020 International Joint Conference on Neural Networks (IJCNN)","location":"Glasgow, United Kingdom","start":{"date-parts":[[2020,7,19]]},"end":{"date-parts":[[2020,7,24]]}},"container-title":["2020 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9200848\/9206590\/09207013.pdf?arnumber=9207013","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,28]],"date-time":"2022-06-28T21:53:36Z","timestamp":1656453216000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9207013\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7]]},"references-count":28,"URL":"https:\/\/doi.org\/10.1109\/ijcnn48605.2020.9207013","relation":{},"subject":[],"published":{"date-parts":[[2020,7]]}}}