{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T16:21:58Z","timestamp":1761582118288,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":25,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T00:00:00Z","timestamp":1625529600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,7,6]]},"DOI":"10.1145\/3468791.3468793","type":"proceedings-article","created":{"date-parts":[[2021,8,12]],"date-time":"2021-08-12T01:03:56Z","timestamp":1628730236000},"page":"1-12","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["UNSUPERVISED ANOMALY DETECTION FOR TIME SERIES WITH OUTLIER EXPOSURE"],"prefix":"10.1145","author":[{"given":"Jiaming","family":"Feng","sequence":"first","affiliation":[{"name":"Shanghai Jiao Tong University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zheng","family":"Huang","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Guo","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weidong","family":"Qiu","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,8,11]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"crossref","unstructured":"Samet Ak\u00e7ay Amir\u00a0Atapour Abarghouei and Toby\u00a0P. Breckon. 2018. GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training. ArXiv abs\/1805.06725(2018).  Samet Ak\u00e7ay Amir\u00a0Atapour Abarghouei and Toby\u00a0P. Breckon. 2018. GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training. ArXiv abs\/1805.06725(2018).","DOI":"10.1007\/978-3-030-20893-6_39"},{"key":"e_1_3_2_1_2_1","unstructured":"Mart\u00edn Arjovsky Soumith Chintala and L\u00e9on Bottou. 2017. Wasserstein GAN. ArXiv abs\/1701.07875(2017).  Mart\u00edn Arjovsky Soumith Chintala and L\u00e9on Bottou. 2017. Wasserstein GAN. ArXiv abs\/1701.07875(2017)."},{"key":"e_1_3_2_1_3_1","volume-title":"Proceedings of the 2011 International Conference on Unsupervised and Transfer Learning Workshop -","volume":"27","author":"Baldi Pierre","year":"2011","unstructured":"Pierre Baldi . 2011 . Autoencoders, Unsupervised Learning and Deep Architectures . In Proceedings of the 2011 International Conference on Unsupervised and Transfer Learning Workshop - Volume 27 (Washington, USA) (UTLW\u201911). JMLR.org, 37\u201350. Pierre Baldi. 2011. Autoencoders, Unsupervised Learning and Deep Architectures. In Proceedings of the 2011 International Conference on Unsupervised and Transfer Learning Workshop - Volume 27 (Washington, USA) (UTLW\u201911). JMLR.org, 37\u201350."},{"key":"e_1_3_2_1_4_1","unstructured":"Ane Bl\u2019azquez-Garc\u2019ia Angel Conde Usue Mori and Jos\u00e9\u00a0Antonio Lozano. 2020. A review on outlier\/anomaly detection in time series data. ArXiv abs\/2002.04236(2020).  Ane Bl\u2019azquez-Garc\u2019ia Angel Conde Usue Mori and Jos\u00e9\u00a0Antonio Lozano. 2020. A review on outlier\/anomaly detection in time series data. ArXiv abs\/2002.04236(2020)."},{"key":"e_1_3_2_1_5_1","unstructured":"Junyoung Chung \u00c7aglar G\u00fcl\u00e7ehre Kyunghyun Cho and Yoshua Bengio. 2014. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. ArXiv abs\/1412.3555(2014).  Junyoung Chung \u00c7aglar G\u00fcl\u00e7ehre Kyunghyun Cho and Yoshua Bengio. 2014. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. ArXiv abs\/1412.3555(2014)."},{"volume-title":"Machine Learning and Knowledge Discovery in Databases, Hendrik Blockeel, Kristian Kersting, Siegfried Nijssen, and Filip \u017delezn\u00fd (Eds.)","author":"Dang Xuan\u00a0Hong","key":"e_1_3_2_1_6_1","unstructured":"Xuan\u00a0Hong Dang , Barbora Micenkov\u00e1 , Ira Assent , and Raymond\u00a0 T. Ng. 2013. Local Outlier Detection with Interpretation . In Machine Learning and Knowledge Discovery in Databases, Hendrik Blockeel, Kristian Kersting, Siegfried Nijssen, and Filip \u017delezn\u00fd (Eds.) . Springer Berlin Heidelberg , Berlin, Heidelberg , 304\u2013320. Xuan\u00a0Hong Dang, Barbora Micenkov\u00e1, Ira Assent, and Raymond\u00a0T. Ng. 2013. Local Outlier Detection with Interpretation. In Machine Learning and Knowledge Discovery in Databases, Hendrik Blockeel, Kristian Kersting, Siegfried Nijssen, and Filip \u017delezn\u00fd (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 304\u2013320."},{"key":"e_1_3_2_1_7_1","unstructured":"Ian\u00a0J. Goodfellow Jean Pouget-Abadie Mehdi Mirza Bing Xu David Warde-Farley Sherjil Ozair Aaron\u00a0C. Courville and Yoshua Bengio. 2014. Generative Adversarial Networks. ArXiv abs\/1406.2661(2014).  Ian\u00a0J. Goodfellow Jean Pouget-Abadie Mehdi Mirza Bing Xu David Warde-Farley Sherjil Ozair Aaron\u00a0C. Courville and Yoshua Bengio. 2014. Generative Adversarial Networks. ArXiv abs\/1406.2661(2014)."},{"key":"e_1_3_2_1_8_1","unstructured":"Dan Hendrycks and Kevin Gimpel. 2017. A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks. ArXiv abs\/1610.02136(2017).  Dan Hendrycks and Kevin Gimpel. 2017. A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks. ArXiv abs\/1610.02136(2017)."},{"key":"e_1_3_2_1_9_1","unstructured":"Dan Hendrycks Mantas Mazeika and Thomas\u00a0G. Dietterich. 2019. Deep Anomaly Detection with Outlier Exposure. ArXiv abs\/1812.04606(2019).  Dan Hendrycks Mantas Mazeika and Thomas\u00a0G. Dietterich. 2019. Deep Anomaly Detection with Outlier Exposure. ArXiv abs\/1812.04606(2019)."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0360-8352(98)00066-7"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"crossref","unstructured":"Xin Jin and Jiawei Han. 2010. K-Means Clustering. Springer US Boston MA 563\u2013564. https:\/\/doi.org\/10.1007\/978-0-387-30164-8_425  Xin Jin and Jiawei Han. 2010. K-Means Clustering. Springer US Boston MA 563\u2013564. https:\/\/doi.org\/10.1007\/978-0-387-30164-8_425","DOI":"10.1007\/978-0-387-30164-8_425"},{"key":"e_1_3_2_1_13_1","volume-title":"Kingma and Jimmy Ba","author":"P.","year":"2015","unstructured":"Diederik\u00a0 P. Kingma and Jimmy Ba . 2015 . Adam : A Method for Stochastic Optimization. CoRR abs\/1412.6980(2015). Diederik\u00a0P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. CoRR abs\/1412.6980(2015)."},{"key":"e_1_3_2_1_14_1","unstructured":"Kimin Lee Honglak Lee Kibok Lee and Jinwoo Shin. 2018. Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. ArXiv abs\/1711.09325(2018).  Kimin Lee Honglak Lee Kibok Lee and Jinwoo Shin. 2018. Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. ArXiv abs\/1711.09325(2018)."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"crossref","unstructured":"Dan Li Dacheng Chen Lei Shi Baihong Jin Jonathan Goh and See-Kiong Ng. 2019. MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks. In ICANN.  Dan Li Dacheng Chen Lei Shi Baihong Jin Jonathan Goh and See-Kiong Ng. 2019. MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks. In ICANN.","DOI":"10.1007\/978-3-030-30490-4_56"},{"key":"e_1_3_2_1_16_1","volume-title":"Isolation Forest. In Proceedings of the 2008 Eighth IEEE International Conference on Data Mining(ICDM \u201908)","author":"Liu Fei\u00a0Tony","year":"2008","unstructured":"Fei\u00a0Tony Liu , Kai\u00a0Ming Ting , and Zhi-Hua Zhou . 2008 . Isolation Forest. In Proceedings of the 2008 Eighth IEEE International Conference on Data Mining(ICDM \u201908) . IEEE Computer Society, USA, 413\u2013422. https:\/\/doi.org\/10.1109\/ICDM. 2008.17 Fei\u00a0Tony Liu, Kai\u00a0Ming Ting, and Zhi-Hua Zhou. 2008. Isolation Forest. In Proceedings of the 2008 Eighth IEEE International Conference on Data Mining(ICDM \u201908). IEEE Computer Society, USA, 413\u2013422. https:\/\/doi.org\/10.1109\/ICDM.2008.17"},{"key":"e_1_3_2_1_17_1","first-page":"0","volume-title":"The 28th European Modeling and Simulation Symposium-EMSS, Larnaca, Cyprus (2016-01-01)","author":"Lopez-Rojas Edgar\u00a0Alonso","year":"2016","unstructured":"Edgar\u00a0Alonso Lopez-Rojas , Ahmad Elmir , and Stefan Axelsson . 2016 . PaySim: A financial mobile money simulator for fraud detection . In The 28th European Modeling and Simulation Symposium-EMSS, Larnaca, Cyprus (2016-01-01) . http:\/\/www.scopus.com\/record\/display.url?origin=inward&partnerID=40&eid=2-s2. 0 - 85002406569 Edgar\u00a0Alonso Lopez-Rojas, Ahmad Elmir, and Stefan Axelsson. 2016. PaySim: A financial mobile money simulator for fraud detection. In The 28th European Modeling and Simulation Symposium-EMSS, Larnaca, Cyprus (2016-01-01). http:\/\/www.scopus.com\/record\/display.url?origin=inward&partnerID=40&eid=2-s2.0-85002406569"},{"key":"e_1_3_2_1_18_1","volume-title":"In ICML Workshop on Deep Learning for Audio, Speech and Language Processing.","author":"Maas L.","year":"2013","unstructured":"Andrew\u00a0 L. Maas , Awni\u00a0 Y. Hannun , and Andrew\u00a0 Y. Ng . 2013 . Rectifier nonlinearities improve neural network acoustic models . In In ICML Workshop on Deep Learning for Audio, Speech and Language Processing. Andrew\u00a0L. Maas, Awni\u00a0Y. Hannun, and Andrew\u00a0Y. Ng. 2013. Rectifier nonlinearities improve neural network acoustic models. In In ICML Workshop on Deep Learning for Audio, Speech and Language Processing."},{"key":"e_1_3_2_1_19_1","unstructured":"Alireza Makhzani Jonathon Shlens Navdeep Jaitly and Ian\u00a0J. Goodfellow. 2015. Adversarial Autoencoders. ArXiv abs\/1511.05644(2015).  Alireza Makhzani Jonathon Shlens Navdeep Jaitly and Ian\u00a0J. Goodfellow. 2015. Adversarial Autoencoders. ArXiv abs\/1511.05644(2015)."},{"volume-title":"k-Nearest Neighbor Classification","author":"Mucherino Antonio","key":"e_1_3_2_1_20_1","unstructured":"Antonio Mucherino , Petraq\u00a0 J. Papajorgji , and Panos\u00a0 M. Pardalos . 2009. k-Nearest Neighbor Classification . Springer New York , New York, NY , 83\u2013106. https:\/\/doi.org\/10.1007\/978-0-387-88615-2_4 Antonio Mucherino, Petraq\u00a0J. Papajorgji, and Panos\u00a0M. Pardalos. 2009. k-Nearest Neighbor Classification. Springer New York, New York, NY, 83\u2013106. https:\/\/doi.org\/10.1007\/978-0-387-88615-2_4"},{"key":"e_1_3_2_1_21_1","volume-title":"C","author":"Nakano Masafumi","year":"2017","unstructured":"Masafumi Nakano , Akihiko Takahashi , and Soichiro Takahashi . 2017. Generalized exponential moving average (EMA) model with particle filtering and anomaly detection. Expert Syst. Appl. 73 , C ( 2017 ), 187\u2013200. https:\/\/doi.org\/10.1016\/j.eswa.2016.12.034 Masafumi Nakano, Akihiko Takahashi, and Soichiro Takahashi. 2017. Generalized exponential moving average (EMA) model with particle filtering and anomaly detection. Expert Syst. Appl. 73, C (2017), 187\u2013200. https:\/\/doi.org\/10.1016\/j.eswa.2016.12.034"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3190508.3190515"},{"key":"e_1_3_2_1_23_1","unstructured":"Marco Schreyer Timur Sattarov Christian Schulze Bernd Reimer and Damian Borth. 2019. Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks. ArXiv abs\/1908.00734(2019).  Marco Schreyer Timur Sattarov Christian Schulze Bernd Reimer and Damian Borth. 2019. Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks. ArXiv abs\/1908.00734(2019)."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330672"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.5555\/1503549.1503556"}],"event":{"name":"SSDBM 2021: 33rd International Conference on Scientific and Statistical Database Management","acronym":"SSDBM 2021","location":"Tampa FL USA"},"container-title":["33rd International Conference on Scientific and Statistical Database Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3468791.3468793","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3468791.3468793","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:17:21Z","timestamp":1750191441000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3468791.3468793"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,6]]},"references-count":25,"alternative-id":["10.1145\/3468791.3468793","10.1145\/3468791"],"URL":"https:\/\/doi.org\/10.1145\/3468791.3468793","relation":{},"subject":[],"published":{"date-parts":[[2021,7,6]]},"assertion":[{"value":"2021-08-11","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}