Online anomaly detection with concept drift adaptation using recurrent neural networks-Reference-Cited by-同舟云学术

Online anomaly detection with concept drift adaptation using recurrent neural networks

Published:2018-01-11 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the ACM India Joint International Conference on Data Science and Management of Data
language:
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Author:

Saurav Sakti¹,Malhotra Pankaj²,TV Vishnu²,Gugulothu Narendhar²,Vig Lovekesh²,Agarwal Puneet²,Shroff Gautam²

Affiliation:

1. IIIT Delhi, New Delhi, India

2. TCS Research, New Delhi, India

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3152494.3152501

Reference48 articles.

1. 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).

2. Multivariate Online Anomaly Detection Using Kernel Recursive Least Squares

3. 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).

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