MOSPAT: AutoML based Model Selection and Parameter Tuning for Time Series Anomaly Detection. (arXiv:2205.11755v1 [cs.LG])

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 …

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