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Organizations leverage anomaly and changepoint detection algorithms to detectchanges in user behavior or service availability and performance. Manyoff-the-shelf detection algorithms, though effective, cannot readily be used inlarge organizations where thousands of users monitor millions of use cases andmetrics with varied time series characteristics and anomaly patterns. Theselection of algorithm and parameters needs to be precise for each use case:manual tuning does not scale, and automated tuning requires ground truth, whichis rarely available.  In this paper, we explore MOSPAT, an end-to-end automated machine learningbased approach for model and parameter selection, combined with a generativemodel to produce labeled data. Our scalable end-to-end system allows individualusers in large organizations to tailor time-series monitoring to their specificuse case and data characteristics, without expert knowledge of anomalydetection algorithms or laborious manual labeling. Our extensive experiments onreal and synthetic data demonstrate that this method consistently outperformsusing any single algorithm.

MOSPAT: AutoML based Model Selection and Parameter Tuning for Time Series Anomaly Detection