  Anomaly detection of time series plays an important role in reliability
systems engineering. However, in practical application, there is no precisely
defined boundary between normal and anomalous behaviors in different
application scenarios. Therefore, different anomaly detection algorithms and
processes ought to be adopted for time series in different situation. Although
such strategy improve the accuracy of anomaly detection, it takes a lot of time
for practitioners to configure various algorithms to millions of series, which
greatly increases the development and maintenance cost of anomaly detection
processes. In this paper, we propose CRATOS which is a self-adapt algorithms
that extract features from time series, and then cluster series with similar
features into one group. For each group we utilize evolutionary algorithm to
search the best anomaly detection methods and processes. Our methods can
significantly reduce the cost of development and maintenance of anomaly
detection. According to experiments, our clustering methods achieves the
state-of-art results. The accuracy of the anomaly detection algorithms in this
paper is 85.1%.
