Abstract
KPI (Key Performance Indicator) anomaly detection is critical for Internet companies to safeguard the availability and stability of services. Many online anomaly detection algorithms have been proposed in recent years. However, they are mainly designed to detect point anomalies, which fail to identify collective or contextual anomalies very well. Thus in this paper, we propose a framework of Collective Contextual Anomaly Detection (CCAD) for KPI data stream. Via Pearson correlation coefficient-based method to adapt to KPI stream, our framework addresses the limitation of time series discords algorithm and achieves a huge improvement in the accuracy for KPI stream. Moreover, instead of using a static threshold, we employ SPOT to generate an automatic threshold to determine anomalies. Extensive experimental results and analysis on multiple public KPIs show the competitive performance of CCAD and the significance of collective contextual anomalies.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Aiops challenge, final dataset. http://iops.ai/dataset_detail/?id=10
Braei, M., Wagner, S.: Anomaly detection in univariate time-series: a survey on the state-of-the-art. arXiv preprint arXiv:2004.00433 (2020)
Hunter, J.S.: The exponentially weighted moving average. J. Qual. Technol. 18(4), 203–210 (1986)
Li, J., Di, S., Shen, Y., Chen, L.: FluxEV: a fast and effective unsupervised framework for time-series anomaly detection. In: Proceedings of the 14th ACM International Conference on Web Search and Data Mining, pp. 824–832 (2021)
Lin, J., Keogh, E., Fu, A., Van Herle, H.: Approximations to magic: finding unusual medical time series. In: 18th IEEE Symposium on Computer-Based Medical Systems (CBMS 2005), pp. 329–334. IEEE (2005)
Liu, D., et al.: Opprentice: towards practical and automatic anomaly detection through machine learning. In: Proceedings of the 2015 Internet Measurement Conference, pp. 211–224 (2015)
Ma, M., Zhang, S., Pei, D., Huang, X., Dai, H.: Robust and rapid adaption for concept drift in software system anomaly detection. In: 2018 IEEE 29th International Symposium on Software Reliability Engineering (ISSRE), pp. 13–24. IEEE (2018)
Ren, H., et al.: Time-series anomaly detection service at Microsoft. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 3009–3017 (2019)
Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoust. Speech Signal Process. 26(1), 43–49 (1978)
Siffer, A., Fouque, P.A., Termier, A., Largouet, C.: Anomaly detection in streams with extreme value theory. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1067–1075 (2017)
Yi, B.K., Faloutsos, C.: Fast time sequence indexing for arbitrary LP norms (2000)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Hu, G., Wang, J., Liu, Y., Ke, W., Lin, Y. (2021). CCAD: A Collective Contextual Anomaly Detection Framework for KPI Data Stream. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Communications in Computer and Information Science, vol 1516. Springer, Cham. https://doi.org/10.1007/978-3-030-92307-5_53
Download citation
DOI: https://doi.org/10.1007/978-3-030-92307-5_53
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-92306-8
Online ISBN: 978-3-030-92307-5
eBook Packages: Computer ScienceComputer Science (R0)