  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.
