Abstract
Time series anomaly detection is a critical task in the domain of Artificial Intelligence for IT Operations (AIOps). Large companies that provide Internet-based services need to closely monitor massive time-series data from applications and hardware in real-time and provide timely troubleshooting to keep reliable services and smooth business. However, selecting and ensembling diverse detectors for better detection results is challenging due to the complexity of time series data. In this paper, an selection framework for time series anomaly detection, which can select proper detectors for time series data of diverse characteristics according to the detector’s performance on historical data. Also, we combine active learning methods to propose unseen samples for labeling, which can significantly alleviate the labeling overhead of operators. Experimental results show the effectiveness of the proposed framework.
C. Wang and T. Yang—These authors contribute equally to this work.
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Acknowledgment
This work is supported by the National Key Research and Development Program of China (2020YFC2003400), the National Natural Science Foundation of China (62172155, 62102425, 62072465), and the Science and Technology Innovation Program of Hunan Province (2021RC2071).
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Wang, C., Yang, T., Cui, J., Li, Y., Zhou, T., Cai, Z. (2022). TSAEns: Ensemble Learning for KPI Anomaly Detection. In: Lai, Y., Wang, T., Jiang, M., Xu, G., Liang, W., Castiglione, A. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2021. Lecture Notes in Computer Science(), vol 13155. Springer, Cham. https://doi.org/10.1007/978-3-030-95384-3_11
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