Ya Su
ABSTRACT
Industry devices (i.e., entities) such as server machines, spacecrafts, engines, etc., are typically monitored with multivariate time series, whose anomaly detection is critical for an entity's service quality management. However, due to the complex temporal dependence and stochasticity of multivariate time series, their anomaly detection remains a big challenge. This paper proposes OmniAnomaly, a stochastic recurrent neural network for multivariate time series anomaly detection that works well robustly for various devices. Its core idea is to capture the normal patterns of multivariate time series by learning their robust representations with key techniques such as stochastic variable connection and planar normalizing flow, reconstruct input data by the representations, and use the reconstruction probabilities to determine anomalies. Moreover, for a detected entity anomaly, OmniAnomaly can provide interpretations based on the reconstruction probabilities of its constituent univariate time series. The evaluation experiments are conducted on two public datasets from aerospace and a new server machine dataset (collected and released by us) from an Internet company. OmniAnomaly achieves an overall F1-Score of 0.86 in three real-world datasets, signicantly outperforming the best performing baseline method by 0.09. The interpretation accuracy for OmniAnomaly is up to 0.89.
- Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014).Google Scholar
Digital Library
- Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron C Courville, and Yoshua Bengio. 2015. A recurrent latent variable model for sequential data. In Advances in neural information processing systems. 2980--2988. Google ScholarDigital Library
- Dhivya Eswaran, Christos Faloutsos, Sudipto Guha, and Nina Mishra. 2018. Spotlight: Detecting anomalies in streaming graphs. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 1378--1386. Google ScholarDigital Library
- 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. arXiv preprint arXiv:1612.06676 (2016).Google Scholar
- Marco Fraccaro, Søren Kaae Sønderby, Ulrich Paquet, and Ole Winther. 2016. Sequential neural models with stochastic layers. In Advances in neural information processing systems. 2199--2207. Google ScholarDigital Library
- Kyle Hundman, Valentino Constantinou, Christopher Laporte, Ian Colwell, and Tom Soderstrom. 2018. Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '18). ACM, New York, NY, USA, 387--395. Google ScholarDigital Library
- Diederik P. Kingma and Max Welling. 2014. Auto-Encoding Variational Bayes. In Proceedings of the Second International Conference on Learning Representations (ICLR 2014).Google Scholar
- Genshiro Kitagawa and Will Gersch. 1996. Linear Gaussian State Space Modeling. In Smoothness Priors Analysis of Time Series. Springer, 55--65.Google Scholar
- Nikolay Laptev, Saeed Amizadeh, and Ian Flint. 2015. Generic and scalable framework for automated time-series anomaly detection. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1939--1947. Google ScholarDigital Library
- Chen Luo, Jian-Guang Lou, Qingwei Lin, Qiang Fu, Rui Ding, Dongmei Zhang, and Zhe Wang. 2014. Correlating events with time series for incident diagnosis. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 1583--1592. Google ScholarDigital Library
- Pankaj Malhotra, Anusha Ramakrishnan, Gaurangi Anand, Lovekesh Vig, Puneet Agarwal, and Gautam Shroff. 2016. LSTM-based encoder-decoder for multi-sensor anomaly detection. arXiv preprint arXiv:1607.00148 (2016).Google Scholar
- Emaad Manzoor, Sadegh M Milajerdi, and Leman Akoglu. 2016. Fast memory-efficient anomaly detection in streaming heterogeneous graphs. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1035--1044. Google ScholarDigital Library
- José-Antonio Mart'inez-Heras and Alessandro Donati. 2014. Enhanced telemetry monitoring with novelty detection. AI Magazine, Vol. 35, 4 (2014), 37--46.Google ScholarCross Ref
- Vinod Nair, Ameya Raul, Shwetabh Khanduja, Vikas Bahirwani, Qihong Shao, Sundararajan Sellamanickam, Sathiya Keerthi, Steve Herbert, and Sudheer Dhulipalla. 2015. Learning a hierarchical monitoring system for detecting and diagnosing service issues. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2029--2038. Google ScholarDigital Library
- Animesh Nandi, Atri Mandal, Shubham Atreja, Gargi B Dasgupta, and Subhrajit Bhattacharya. 2016. Anomaly detection using program control flow graph mining from execution logs. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 215--224. Google ScholarDigital Library
- Daehyung Park, Yuuna Hoshi, and Charles C. Kemp. 2018. A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-Based Variational Autoencoder. IEEE Robotics and Automation Letters, Vol. 3 (2018), 1544--1551.Google ScholarCross Ref
- Daehyung Park, Hokeun Kim, Yuuna Hoshi, Zackory Erickson, Ariel Kapusta, and Charles C Kemp. 2017. A multimodal execution monitor with anomaly classification for robot-assisted feeding. In Intelligent Robots and Systems (IROS), 2017 IEEE/RSJ International Conference on. IEEE, 5406--5413.Google ScholarCross Ref
- Danilo Jimenez Rezende and Shakir Mohamed. 2015. Variational Inference with Normalizing Flows. Computer Science (2015), 1530--1538.Google Scholar
- Christian Robert and George Casella. 2013. Monte Carlo statistical methods .Springer Science & Business Media. Google Scholar
- A. Rodriguez, D. Bourne, M. Mason, G. F. Rossano, and J. Wang. 2010. Failure detection in assembly: Force signature analysis. In 2010 IEEE International Conference on Automation Science and Engineering. 210--215.Google Scholar
- Terrence J Sejnowski and Charles R Rosenberg. 1987. Parallel networks that learn to pronounce English text. Complex systems, Vol. 1, 1 (1987), 145--168.Google Scholar
- Alban Siffer, Pierre-Alain Fouque, Alexandre Termier, and Christine Largouet. 2017. Anomaly detection in streams with extreme value theory. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1067--1075. Google ScholarDigital Library
- Fei Wu, Pranay Anchuri, and Zhenhui Li. 2017. Structural event detection from log messages. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1175--1184. Google ScholarDigital Library
- Haowen Xu, Wenxiao Chen, Nengwen Zhao, Zeyan Li, Jiahao Bu, Zhihan Li, Ying Liu, Youjian Zhao, Dan Pei, Yang Feng, et almbox. 2018. Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications. In Proceedings of the 2018 World Wide Web Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 187--196. Google ScholarDigital Library
- Xiwang Yang, Harald Steck, Yang Guo, and Yong Liu. 2012. On top-k recommendation using social networks. In Proceedings of the sixth ACM conference on Recommender systems. ACM, 67--74. Google ScholarDigital Library
- Arthur Zimek, Erich Schubert, and Hans-Peter Kriegel. 2012. A survey on unsupervised outlier detection in high-dimensional numerical data. Statistical Analysis and Data Mining: The ASA Data Science Journal, Vol. 5, 5 (2012), 363--387. Google ScholarDigital Library
- Bo Zong, Qi Song, Martin Renqiang Min, Wei Cheng, Cristian Lumezanu, Daeki Cho, and Haifeng Chen. 2018. Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In International Conference on Learning Representations.Google Scholar
Index Terms
- Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network
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