{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T03:18:03Z","timestamp":1792725483029,"version":"4.5.0"},"reference-count":41,"publisher":"IEEE","license":[{"start":{"date-parts":[[2020,12,10]],"date-time":"2020-12-10T00:00:00Z","timestamp":1607558400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,12,10]],"date-time":"2020-12-10T00:00:00Z","timestamp":1607558400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,12,10]],"date-time":"2020-12-10T00:00:00Z","timestamp":1607558400000},"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":[[2020,12,10]]},"DOI":"10.1109\/bigdata50022.2020.9378192","type":"proceedings-article","created":{"date-parts":[[2021,3,19]],"date-time":"2021-03-19T17:10:21Z","timestamp":1616173821000},"page":"5068-5077","source":"Crossref","is-referenced-by-count":50,"title":["An Autocorrelation-based LSTM-Autoencoder for Anomaly Detection on Time-Series Data"],"prefix":"10.1109","author":[{"given":"Hajar","family":"Homayouni","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sudipto","family":"Ghosh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Indrakshi","family":"Ray","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shlok","family":"Gondalia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jerry","family":"Duggan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael G.","family":"Kahn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ISCO.2017.7855983"},{"key":"ref38","article-title":"H2o Random Forest","year":"0"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098052"},{"key":"ref32","first-page":"281","article-title":"Random Search for Hyper-parameter Optimization","volume":"13","author":"bergstra","year":"2012","journal-title":"Journal of Machine Learning Research"},{"key":"ref31","author":"park","year":"2017","journal-title":"Fundamentals of Probability and Stochastic Processes with Applications to Communications"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/DEPCOS-RELCOMEX.2006.38"},{"key":"ref37","author":"bishop","year":"2011","journal-title":"Pattern Recognition and Machine Learning"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/s13347-017-0293-z"},{"key":"ref35","article-title":"Outlier Detection Datasets","year":"0"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/s10100-017-0479-6"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/SIEDS.2019.8735594"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/bs.adcom.2018.03.015"},{"key":"ref11","first-page":"1544","article-title":"A MultimodalAnomaly Detector for Robot-Assisted Feeding Using an LSTM-Based Variational Autoencoder","volume":"3","author":"park","year":"2018","journal-title":"RA-L"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/PHM-Paris.2019.00055"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-16248-0_54"},{"key":"ref14","article-title":"Yahoo Server Traffic: A Benchmark Dataset for Time Series Anomaly Detection","year":"0"},{"key":"ref15","article-title":"NASA Shuttle","year":"0"},{"key":"ref16","article-title":"Energy Data","year":"0"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1080\/10618600.2019.1617160"},{"key":"ref18","article-title":"TSfeatures from CRAN Library","year":"0"},{"key":"ref19","author":"adhikari","year":"2013","journal-title":"An introductory study on time series modeling and forecasting"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CCECE.2018.8447597"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1144\/geochem2016-024"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.08.026"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33011409"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3338840.3355641"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-30671-1_4"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/342009.335388"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2870151"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-15-3992-3_42"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/DSAA.2016.92"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1162\/neco_a_01199"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/MDM.2018.00029"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICCIKE47802.2019.9004325"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2788611"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2015.104"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.envsoft.2018.06.004"},{"key":"ref41","first-page":"347","article-title":"How Good is 85%? A Survey Tool to Connect Classifier Evaluation to Acceptability of Accuracy","author":"kay","year":"2015","journal-title":"Proc of CHI"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.scs.2017.08.009"},{"key":"ref26","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780198538493.001.0001","author":"bishop","year":"1995","journal-title":"Neural Networks for Pattern Recognition"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-03493-1_41"}],"event":{"name":"2020 IEEE International Conference on Big Data (Big Data)","location":"Atlanta, GA, USA","start":{"date-parts":[[2020,12,10]]},"end":{"date-parts":[[2020,12,13]]}},"container-title":["2020 IEEE International Conference on Big Data (Big Data)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9377717\/9377728\/09378192.pdf?arnumber=9378192","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,26]],"date-time":"2024-08-26T07:43:11Z","timestamp":1724658191000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9378192\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,10]]},"references-count":41,"URL":"https:\/\/doi.org\/10.1109\/bigdata50022.2020.9378192","relation":{},"subject":[],"published":{"date-parts":[[2020,12,10]]}}}