NLP Based Anomaly Detection for Categorical Time Series
Pages 27 - 34
Abstract
Identifying anomalies in large multi-dimensional time series is a crucial and difficult task across multiple domains. Few methods exist in the literature that address this task when some of the variables are categorical in nature. We formalize an analogy between categorical time series and classical Natural Language Processing and demonstrate the strength of this analogy for anomaly detection and root cause investigation by implementing and testing three different machine learning anomaly detection and root cause investigation models based upon it.
References
[1]
G. Jiang, H. Chen, and K. Yoshihira, “Modeling and tracking of transaction flow dynamics for fault detection in complex systems,” IEEE Transactions on Dependable and Secure Computing, vol. 3, no. 4, pp. 312–326, 2006.
[2]
Y. Su, Y. Zhao, C. Niu, R. Liu, W. Sun, and D. Pei, “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, 2019, pp. 2828–2837.
[3]
K. Hundman, V. Constantinou, C. Laporte, I. Colwell, and T. Soderstrom, “Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding,” in Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, 2018, pp. 387–395.
[4]
S. C. Tan, K. M. Ting, and T. F. Liu, “Fast anomaly detection for streaming data,” in Twenty-Second International Joint Conference on Artificial Intelligence, 2011.
[5]
E. Keogh, J. Lin, and A. Fu, “Hot sax: Efficiently finding the most unusual time series subsequence,” in Fifth IEEE International Conference on Data Mining (ICDM'05). IEEE, 2005, pp. 8-pp.
[6]
C. Guo and F. Berkhahn, “Entity embeddings of categorical variables,” arXiv preprint arXiv:, 2016.
[7]
I. Padhi, Y. Schiff, I. Melnyk, M. Rigotti, Y. Mroueh, P. Dognin, J. Ross, R. Nair, and E. Altman, “Tabular transformers for modeling multivariate time series,” in ICASSP 2021–2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021, pp. 3565–3569.
[8]
R. Gwadera, M. J. Atallah, and W. Szpankowski, “Reliable detection of episodes in event sequences,” Knowledge and Information Systems, vol. 7, no. 4, pp. 415–437, 2005.
[9]
S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, and R. Harshman, “Indexing by latent semantic analysis,” Journal of the American society for information science, vol. 41, no. 6, pp. 391–407, 1990.
[10]
G. Zweig, J. C. Platt, C. Meek, C. J. Burges, A. Yessenalina, and Q. Liu, “Computational approaches to sentence completion,” in Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2012, pp. 601–610.
[11]
C. Eckart and G. Young, “The approximation of one matrix by another of lower rank,” Psychometrika, vol. 1, no. 3, pp. 211–218, 1936.
[12]
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems, vol. 30, 2017.
[13]
T. He, X. Tan, Y. Xia, D. He, T. Qin, Z. Chen, and T.-Y. Liu, “Layer-wise coordination between encoder and decoder for neural machine translation,” Advances in Neural Information Processing Systems, vol. 31, 2018.
[14]
Y. Peng, C. Haifeng, and M. Sato, “3D histogram based anomaly detection for categorical sensor data in internet of things,” to appear in the International Workshop on VLIoT, 2022.
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Aug 2022
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Published: 09 August 2022
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