Organizations leverage anomaly and changepoint detection algorithms to detect changes in user behavior or service availability and performance. Many off-the-shelf detection algorithms, though effective, cannot readily be used in large organizations where thousands of users monitor millions of use cases and metrics with varied time series characteristics and anomaly patterns. The selection of algorithm and parameters needs to be precise for each use case: manual tuning does not scale, and automated tuning requires ground truth, which is rarely available. In this paper, we explore MOSPAT, an end-to-end automated machine learning based approach for model and parameter selection, combined with a generative model to produce labeled data. Our scalable end-to-end system allows individual users in large organizations to tailor time-series monitoring to their specific use case and data characteristics, without expert knowledge of anomaly detection algorithms or laborious manual labeling. Our extensive experiments on real and synthetic data demonstrate that this method consistently outperforms using any single algorithm.

本文提出了一种基于自动生成标记数据的端到端自动化机器学习方法MOSPAT，以选择算法和参数，可以满足大规模组织中用户所监测的无数用例和指标以及其异常模式的各种时间序列特征。该方法不需要专家知识或繁琐的手动标注，并在实验数据上稳定优于使用任何单一算法。

基于AutoML的时间序列异常检测模型选择和参数调整 (MOSPAT)