{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,7]],"date-time":"2026-06-07T07:58:22Z","timestamp":1780819102365,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":48,"publisher":"ACM","license":[{"start":{"date-parts":[[2018,1,11]],"date-time":"2018-01-11T00:00:00Z","timestamp":1515628800000},"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":[[2018,1,11]]},"DOI":"10.1145\/3152494.3152501","type":"proceedings-article","created":{"date-parts":[[2018,2,13]],"date-time":"2018-02-13T15:28:46Z","timestamp":1518535726000},"page":"78-87","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":74,"title":["Online anomaly detection with concept drift adaptation using recurrent neural networks"],"prefix":"10.1145","author":[{"given":"Sakti","family":"Saurav","sequence":"first","affiliation":[{"name":"IIIT Delhi, New Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pankaj","family":"Malhotra","sequence":"additional","affiliation":[{"name":"TCS Research, New Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vishnu","family":"TV","sequence":"additional","affiliation":[{"name":"TCS Research, New Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Narendhar","family":"Gugulothu","sequence":"additional","affiliation":[{"name":"TCS Research, New Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lovekesh","family":"Vig","sequence":"additional","affiliation":[{"name":"TCS Research, New Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Puneet","family":"Agarwal","sequence":"additional","affiliation":[{"name":"TCS Research, New Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gautam","family":"Shroff","sequence":"additional","affiliation":[{"name":"TCS Research, New Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2018,1,11]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Real-Time Anomaly Detection for Streaming Analytics. arXiv preprint arXiv:1607.02480","author":"Ahmad Subutai","year":"2016","unstructured":"Subutai Ahmad and Scott Purdy . 2016. Real-Time Anomaly Detection for Streaming Analytics. arXiv preprint arXiv:1607.02480 ( 2016 ). Subutai Ahmad and Scott Purdy. 2016. Real-Time Anomaly Detection for Streaming Analytics. arXiv preprint arXiv:1607.02480 (2016)."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.5555\/1811380.1811547"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFCOM.2007.79"},{"key":"e_1_3_2_1_4_1","volume-title":"NIPS 2015 Workshop: Machine Learning for eCommerce.","author":"Anand Gaurangi","year":"2015","unstructured":"Gaurangi Anand , Auon H. Kazmi , Pankaj Malhotra , Lovekesh Vig , Puneet Agarwal , and Gautam Shroff . 2015 . Deep Temporal Features to Predict Repeat Buyers . In NIPS 2015 Workshop: Machine Learning for eCommerce. Gaurangi Anand, Auon H. Kazmi, Pankaj Malhotra, Lovekesh Vig, Puneet Agarwal, and Gautam Shroff. 2015. Deep Temporal Features to Predict Repeat Buyers. In NIPS 2015 Workshop: Machine Learning for eCommerce."},{"key":"e_1_3_2_1_5_1","volume-title":"Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473","author":"Bahdanau Dzmitry","year":"2014","unstructured":"Dzmitry Bahdanau , Kyunghyun Cho , and Yoshua Bengio . 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 ( 2014 ). Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)."},{"key":"e_1_3_2_1_6_1","volume-title":"Collective Anomaly Detection Based on Long Short-Term Memory Recurrent Neural Networks. In International Conference on Future Data and Security Engineering. Springer, 141--152","author":"Bontemps Lo\u00efc","year":"2016","unstructured":"Lo\u00efc Bontemps , James McDermott , Nhien-An Le-Khac , 2016 . Collective Anomaly Detection Based on Long Short-Term Memory Recurrent Neural Networks. In International Conference on Future Data and Security Engineering. Springer, 141--152 . Lo\u00efc Bontemps, James McDermott, Nhien-An Le-Khac, et al. 2016. Collective Anomaly Detection Based on Long Short-Term Memory Recurrent Neural Networks. In International Conference on Future Data and Security Engineering. Springer, 141--152."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/DSAA.2015.7344872"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICNP.2016.7785326"},{"key":"e_1_3_2_1_9_1","volume-title":"Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio.","author":"Cho Kyunghyun","year":"2014","unstructured":"Kyunghyun Cho , Bart Van Merri\u00ebnboer , Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014 . Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014). Kyunghyun Cho, Bart Van Merri\u00ebnboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2014.2300753"},{"key":"e_1_3_2_1_11_1","volume-title":"NIPS Time Series Workshop 2016","author":"Filonov Pavel","year":"2016","unstructured":"Pavel Filonov , Andrey Lavrentyev , and Artem Vorontsov . 2016 . Multivariate Industrial Time Series with Cyber-Attack Simulation: Fault Detection Using an LSTM-based Predictive Data Model . NIPS Time Series Workshop 2016 , arXiv preprint arXiv:1612.06676 (2016). Pavel Filonov, Andrey Lavrentyev, and Artem Vorontsov. 2016. Multivariate Industrial Time Series with Cyber-Attack Simulation: Fault Detection Using an LSTM-based Predictive Data Model. NIPS Time Series Workshop 2016, arXiv preprint arXiv:1612.06676 (2016)."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2523813"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/347090.347189"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"e_1_3_2_1_15_1","volume-title":"2nd ACM SIGKDD Workshop on ML for PHM. arXiv preprint arXiv:1709","author":"Gugulothu Narendhar","year":"2017","unstructured":"Narendhar Gugulothu , Vishnu TV , Pankaj Malhotra , Lovekesh Vig , Puneet Agarwal , and Gautam Shroff . 2017 . Predicting Remaining Useful Life using Time Series Embeddings based on Recurrent Neural Networks . 2nd ACM SIGKDD Workshop on ML for PHM. arXiv preprint arXiv:1709 .01073 (2017). Narendhar Gugulothu, Vishnu TV, Pankaj Malhotra, Lovekesh Vig, Puneet Agarwal, and Gautam Shroff. 2017. Predicting Remaining Useful Life using Time Series Embeddings based on Recurrent Neural Networks. 2nd ACM SIGKDD Workshop on ML for PHM. arXiv preprint arXiv:1709.01073 (2017)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/DSAA.2016.92"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2013.184"},{"key":"e_1_3_2_1_18_1","unstructured":"Michiel Hermans and Benjamin Schrauwen. 2013. Training and analysing deep recurrent neural networks. In Advances in Neural Information Processing Systems. 190--198.   Michiel Hermans and Benjamin Schrauwen. 2013. Training and analysing deep recurrent neural networks. In Advances in Neural Information Processing Systems. 190--198."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_1_20_1","unstructured":"Nikolay Laptev Saeed Amizadeh and Y. Billawala. 2015. Yahoo Labs News: Announcing A Benchmark Dataset For Time Series Anomaly Detection {Online blog}. Available: http:\/\/labs.yahoo.com\/news\/announcing-a-benchmark-datasetfor-time-series-anomaly-detection.  Nikolay Laptev Saeed Amizadeh and Y. Billawala. 2015. Yahoo Labs News: Announcing A Benchmark Dataset For Time Series Anomaly Detection {Online blog}. Available: http:\/\/labs.yahoo.com\/news\/announcing-a-benchmark-datasetfor-time-series-anomaly-detection."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2015.141"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-007-0064-z"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2003.1223670"},{"key":"e_1_3_2_1_24_1","volume-title":"LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection. In Anomaly Detection Workshop at 33rd International Conference on Machine Learning. arXiv preprint arxiv:1607","author":"Malhotra Pankaj","year":"2016","unstructured":"Pankaj Malhotra , Anusha Ramakrishnan , Gaurangi Anand , Lovekesh Vig , Puneet Agarwal , and Gautam Shroff . 2016 . LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection. In Anomaly Detection Workshop at 33rd International Conference on Machine Learning. arXiv preprint arxiv:1607 .00148 (2016). Pankaj Malhotra, Anusha Ramakrishnan, Gaurangi Anand, Lovekesh Vig, Puneet Agarwal, and Gautam Shroff. 2016. LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection. In Anomaly Detection Workshop at 33rd International Conference on Machine Learning. arXiv preprint arxiv:1607.00148 (2016)."},{"key":"e_1_3_2_1_25_1","volume-title":"1st ACM SIGKDD Workshop on ML for PHM. arXiv preprint arXiv:1608","author":"Malhotra Pankaj","year":"2016","unstructured":"Pankaj Malhotra , Vishnu TV , Anusha Ramakrishnan , Gaurangi Anand , Lovekesh Vig , Puneet Agarwal , and Gautam Shroff . 2016 . Multi-Sensor Prognostics using an Unsupervised Health Index based on LSTM Encoder-Decoder . 1st ACM SIGKDD Workshop on ML for PHM. arXiv preprint arXiv:1608 .06154 (2016). Pankaj Malhotra, Vishnu TV, Anusha Ramakrishnan, Gaurangi Anand, Lovekesh Vig, Puneet Agarwal, and Gautam Shroff. 2016. Multi-Sensor Prognostics using an Unsupervised Health Index based on LSTM Encoder-Decoder. 1st ACM SIGKDD Workshop on ML for PHM. arXiv preprint arXiv:1608.06154 (2016)."},{"key":"e_1_3_2_1_26_1","volume-title":"25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 607--612","author":"Malhotra Pankaj","year":"2017","unstructured":"Pankaj Malhotra , Vishnu TV , Lovekesh Vig , Puneet Agarwal , and Gautam Shroff . 2017 . TimeNet: Pre-trained deep recurrent neural network for time series classification . In 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 607--612 . Pankaj Malhotra, Vishnu TV, Lovekesh Vig, Puneet Agarwal, and Gautam Shroff. 2017. TimeNet: Pre-trained deep recurrent neural network for time series classification. In 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 607--612."},{"key":"e_1_3_2_1_27_1","volume-title":"23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 89--94","author":"Malhotra Pankaj","year":"2015","unstructured":"Pankaj Malhotra , Lovekesh Vig , Gautam Shroff , and Puneet Agarwal . 2015 . Long Short Term Memory Networks for Anomaly Detection in Time Series. In ESANN , 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 89--94 . Pankaj Malhotra, Lovekesh Vig, Gautam Shroff, and Puneet Agarwal. 2015. Long Short Term Memory Networks for Anomaly Detection in Time Series. In ESANN, 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 89--94."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICFHR.2014.55"},{"key":"e_1_3_2_1_29_1","volume-title":"State-of-the-art in sequential change-point detection. Methodology and computing in applied probability 14, 3","author":"Polunchenko Aleksey S","year":"2012","unstructured":"Aleksey S Polunchenko and Alexander G Tartakovsky . 2012. State-of-the-art in sequential change-point detection. Methodology and computing in applied probability 14, 3 ( 2012 ), 649--684. Aleksey S Polunchenko and Alexander G Tartakovsky. 2012. State-of-the-art in sequential change-point detection. Methodology and computing in applied probability 14, 3 (2012), 649--684."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-008-5093-3"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2015.7363865"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1022810614389"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.5555\/2627435.2670313"},{"key":"e_1_3_2_1_34_1","volume-title":"International Conference on Machine Learning. 843--852","author":"Srivastava Nitish","year":"2015","unstructured":"Nitish Srivastava , Elman Mansimov , and Ruslan Salakhudinov . 2015 . Unsupervised learning of video representations using lstms . In International Conference on Machine Learning. 843--852 . Nitish Srivastava, Elman Mansimov, and Ruslan Salakhudinov. 2015. Unsupervised learning of video representations using lstms. In International Conference on Machine Learning. 843--852."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2012.2233713"},{"key":"e_1_3_2_1_36_1","volume-title":"The problem of concept drift: definitions and related work. Computer Science Department","author":"Tsymbal Alexey","year":"2004","unstructured":"Alexey Tsymbal . 2004. The problem of concept drift: definitions and related work. Computer Science Department , Trinity College Dublin 106, 2 ( 2004 ). Alexey Tsymbal. 2004. The problem of concept drift: definitions and related work. Computer Science Department, Trinity College Dublin 106, 2 (2004)."},{"key":"e_1_3_2_1_37_1","volume-title":"Bayesian Networks for Interpretable Health Monitoring of Complex Systems. Workshop on AI for Internet of Things at IJCAI","author":"Narendhar Gugulothu Vishnu TV","year":"2017","unstructured":"Vishnu TV , Narendhar Gugulothu , Pankaj Malhotra , Lovekesh Vig , Puneet Agarwal , and Gautam Shroff . 2017 . Bayesian Networks for Interpretable Health Monitoring of Complex Systems. Workshop on AI for Internet of Things at IJCAI (2017). Vishnu TV, Narendhar Gugulothu, Pankaj Malhotra, Lovekesh Vig, Puneet Agarwal, and Gautam Shroff. 2017. Bayesian Networks for Interpretable Health Monitoring of Complex Systems. Workshop on AI for Internet of Things at IJCAI (2017)."},{"key":"e_1_3_2_1_38_1","volume-title":"Visualizing LSTM decisions. arXiv preprint arXiv:1705.08153","author":"van der Westhuizen Jos","year":"2017","unstructured":"Jos van der Westhuizen and Joan Lasenby . 2017. Visualizing LSTM decisions. arXiv preprint arXiv:1705.08153 ( 2017 ). Jos van der Westhuizen and Joan Lasenby. 2017. Visualizing LSTM decisions. arXiv preprint arXiv:1705.08153 (2017)."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/INM.2011.5990537"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/2983323.2983344"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.58337"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1989.1.2.270"},{"key":"e_1_3_2_1_43_1","volume-title":"NIPS 2015 Time Series Workshop. arXiv:1605","author":"Yadav Mohit","year":"2015","unstructured":"Mohit Yadav , Pankaj Malhotra , Lovekesh Vig , K Sriram , and Gautam Shroff . 2015 . ODE-augmented Training Improves Anomaly Detection in Sensor Data from Machines . In NIPS 2015 Time Series Workshop. arXiv:1605 .01534. Mohit Yadav, Pankaj Malhotra, Lovekesh Vig, K Sriram, and Gautam Shroff. 2015. ODE-augmented Training Improves Anomaly Detection in Sensor Data from Machines. In NIPS 2015 Time Series Workshop. arXiv:1605.01534."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/775047.775148"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.peva.2010.08.018"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7299101"},{"key":"e_1_3_2_1_47_1","volume-title":"International Conference on Machine Learning. 1100--1109","author":"Zhai Shuangfei","year":"2016","unstructured":"Shuangfei Zhai , Yu Cheng , Weining Lu , and Zhongfei Zhang . 2016 . Deep structured energy based models for anomaly detection . In International Conference on Machine Learning. 1100--1109 . Shuangfei Zhai, Yu Cheng, Weining Lu, and Zhongfei Zhang. 2016. Deep structured energy based models for anomaly detection. In International Conference on Machine Learning. 1100--1109."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.3390\/s17020273"}],"event":{"name":"CoDS-COMAD '18: The ACM India Joint International Conference on Data Science & Management of Data","location":"Goa India","acronym":"CoDS-COMAD '18"},"container-title":["Proceedings of the ACM India Joint International Conference on Data Science and Management of Data"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3152494.3152501","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3152494.3152501","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T02:13:31Z","timestamp":1750212811000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3152494.3152501"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,1,11]]},"references-count":48,"alternative-id":["10.1145\/3152494.3152501","10.1145\/3152494"],"URL":"https:\/\/doi.org\/10.1145\/3152494.3152501","relation":{},"subject":[],"published":{"date-parts":[[2018,1,11]]},"assertion":[{"value":"2018-01-11","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}