Qi Guo
ABSTRACT
Real-world scenarios such as Internet, industrial equipment and finance field generate a large number of multivariate time series all the time which are important for describing the operational state of a system. Therefore, anomaly detection on the multivariate time series has become a hot topic today. How to utilize regularization to eliminate overfitting is an important issue since it inhibits the representative power of existing models. In this paper, a reconstruction model called Autoregressive Graph Adversarial Network (ARGAN) is proposed. First, we develop a latent space reconstruction strategy to guarantee ARGAN’s representative ability for the key features. Then, the autoregressive regularization using temporal dependency is proposed to inhibit overfitting. Finally, a regularized annealing strategy is designed to balance reconstruction and regularization. The proposed model can achieve better performance on four real-world datasets compared with other six algorithms.
- Ahmed Abdulaal, Zhuanghua Liu, and Tomer Lancewicki. 2021. Practical Approach to Asynchronous Multivariate Time Series Anomaly Detection and Localization. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining(KDD ’21). Association for Computing Machinery, New York, NY, USA, 2485–2494. https://doi.org/10.1145/3447548.3467174Google Scholar
Digital Library
- Julien Audibert, Pietro Michiardi, Frédéric Guyard, Sébastien Marti, and Maria A. Zuluaga. 2020. USAD: UnSupervised Anomaly Detection on Multivariate Time Series. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining(KDD ’20). Association for Computing Machinery, New York, NY, USA, 3395–3404. https://doi.org/10.1145/3394486.3403392Google ScholarDigital Library
- Xuanhao Chen, Liwei Deng, Feiteng Huang, Chengwei Zhang, Zongquan Zhang, Yan Zhao, and Kai Zheng. 2021. DAEMON: Unsupervised Anomaly Detection and Interpretation for Multivariate Time Series. In 2021 IEEE 37th International Conference on Data Engineering (ICDE). 2225–2230. https://doi.org/10.1109/ICDE51399.2021.00228Google ScholarCross Ref
- Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. Advances in Neural Information Processing Systems 29 (2016), 3844–3852.Google ScholarDigital Library
- Ailin Deng and Bryan Hooi. 2021. Graph Neural Network-Based Anomaly Detection in Multivariate Time Series. arXiv:2106.06947 [cs] (June 2021). arxiv:2106.06947 [cs]Google Scholar
- Nan Ding, Huanbo Gao, Hongyu Bu, Haoxuan Ma, and Huaiwei Si. 2018. Multivariate-Time-Series-Driven Real-time Anomaly Detection Based on Bayesian Network. Sensors 18, 10 (Oct. 2018), 3367. https://doi.org/10.3390/s18103367Google ScholarCross Ref
- Jonathan Goh, Sridhar Adepu, Khurum Nazir Junejo, and Aditya Mathur. 2017. A Dataset to Support Research in the Design of Secure Water Treatment Systems. In Critical Information Infrastructures Security(Lecture Notes in Computer Science), Grigore Havarneanu, Roberto Setola, Hypatia Nassopoulos, and Stephen Wolthusen (Eds.). Springer International Publishing, Cham, 88–99. https://doi.org/10.1007/978-3-319-71368-7_8Google ScholarCross Ref
- Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative Adversarial Nets. In Advances in Neural Information Processing Systems, Vol. 27. Curran Associates, Inc.Google Scholar
- 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). Association for Computing Machinery, New York, NY, USA, 387–395. https://doi.org/10.1145/3219819.3219845Google ScholarDigital Library
- Dan Li, Dacheng Chen, Baihong Jin, Lei Shi, Jonathan Goh, and See-Kiong Ng. 2019. MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks. In Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series(Lecture Notes in Computer Science), Igor V. Tetko, Věra Kůrková, Pavel Karpov, and Fabian Theis (Eds.). Springer International Publishing, Cham, 703–716. https://doi.org/10.1007/978-3-030-30490-4_56Google ScholarDigital Library
- Zhihan Li, Youjian Zhao, Jiaqi Han, Ya Su, Rui Jiao, Xidao Wen, and Dan Pei. 2021. Multivariate Time Series Anomaly Detection and Interpretation Using Hierarchical Inter-Metric and Temporal Embedding. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining(KDD ’21). Association for Computing Machinery, New York, NY, USA, 3220–3230. https://doi.org/10.1145/3447548.3467075Google ScholarDigital Library
- Haoran Liang, Lei Song, Jianxing Wang, Lili Guo, Xuzhi Li, and Ji Liang. 2021. Robust Unsupervised Anomaly Detection via Multi-Time Scale DCGANs with Forgetting Mechanism for Industrial Multivariate Time Series. Neurocomputing 423 (Jan. 2021), 444–462. https://doi.org/10.1016/j.neucom.2020.10.084Google ScholarCross Ref
- Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou. 2008. Isolation Forest. In 2008 Eighth IEEE International Conference on Data Mining. 413–422. https://doi.org/10.1109/ICDM.2008.17Google ScholarDigital Library
- Aditya P. Mathur and Nils Ole Tippenhauer. 2016. SWaT: A Water Treatment Testbed for Research and Training on ICS Security. In 2016 International Workshop on Cyber-physical Systems for Smart Water Networks (CySWater). 31–36. https://doi.org/10.1109/CySWater.2016.7469060Google ScholarCross Ref
- Sho Ooi, Tsuyoshi Ikegaya, Mutsuo Sano, Hajime Tabuchi, Fumie Saito, and Satoshi Umeda. 2017. Attention Behavior Evaluation during Daily Living based on Egocentric Vision. Journal of Advances in Information Technology Vol 8, 2 (2017).Google Scholar
- 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 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 5406–5413. https://doi.org/10.1109/IROS.2017.8206437Google ScholarDigital Library
- Santi Phithakkitnukoon and Carlo Ratti. 2010. A recent-pattern biased dimension-reduction framework for time series data. Journal of Advances in Information Technology 1, 4 (2010), 168–180.Google ScholarCross Ref
- Hansheng Ren, Bixiong Xu, Yujing Wang, Chao Yi, Congrui Huang, Xiaoyu Kou, Tony Xing, Mao Yang, Jie Tong, and Qi Zhang. 2019. Time-Series Anomaly Detection Service at Microsoft. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining(KDD ’19). Association for Computing Machinery, New York, NY, USA, 3009–3017. https://doi.org/10.1145/3292500.3330680Google ScholarDigital Library
- Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen, and Xi Chen. 2016. Improved Techniques for Training GANs. In Advances in Neural Information Processing Systems, Vol. 29. Curran Associates, Inc.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(KDD ’17). Association for Computing Machinery, New York, NY, USA, 1067–1075. https://doi.org/10.1145/3097983.3098144Google ScholarDigital Library
- Ya Su, Youjian Zhao, Chenhao Niu, Rong Liu, Wei Sun, and Dan Pei. 2019. Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining(KDD ’19). Association for Computing Machinery, New York, NY, USA, 2828–2837. https://doi.org/10.1145/3292500.3330672Google ScholarDigital Library
- Haowen Xu, Wenxiao Chen, Nengwen Zhao, Zeyan Li, Jiahao Bu, Zhihan Li, Ying Liu, Youjian Zhao, Dan Pei, Yang Feng, Jie Chen, Zhaogang Wang, and Honglin Qiao. 2018. Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications. In Proceedings of the 2018 World Wide Web Conference(WWW ’18). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 187–196. https://doi.org/10.1145/3178876.3185996Google ScholarDigital Library
- Chunyong Yin, Sun Zhang, Jin Wang, and Neal N. Xiong. 2020. Anomaly Detection Based on Convolutional Recurrent Autoencoder for IoT Time Series. IEEE Transactions on Systems, Man, and Cybernetics: Systems (2020), 1–11. https://doi.org/10.1109/TSMC.2020.2968516Google ScholarCross Ref
- Hang Zhao, Yujing Wang, Juanyong Duan, Congrui Huang, Defu Cao, Yunhai Tong, Bixiong Xu, Jing Bai, Jie Tong, and Qi Zhang. 2020. Multivariate Time-series Anomaly Detection via Graph Attention Network. arXiv:2009.02040 [cs, stat] (Sept. 2020). arxiv:2009.02040 [cs, stat]Google Scholar
- 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
- Multivariate Time Series Anomaly Detection in a Regularization Perspective
Recommendations
Adversarial Autoencoder for Unsupervised Time Series Anomaly Detection and Interpretation
WSDM '23: Proceedings of the Sixteenth ACM International Conference on Web Search and Data MiningIn many complex systems, devices are typically monitored and generating massive multivariate time series. However, due to the complex patterns and little useful labeled data, it is a great challenge to detect anomalies from these time series data. ...
Learning Sparse Latent Graph Representations for Anomaly Detection in Multivariate Time Series
KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data MiningAnomaly detection in high-dimensional time series is typically tackled using either reconstruction- or forecasting-based algorithms due to their abilities to learn compressed data representations and model temporal dependencies, respectively. However, ...
Multivariate Time Series Anomaly Detection and Interpretation using Hierarchical Inter-Metric and Temporal Embedding
KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data MiningAnomaly detection is a crucial task for monitoring various status (i.e., metrics) of entities (e.g., manufacturing systems and Internet services), which are often characterized by multivariate time series (MTS). In practice, it's important to precisely ...
Comments