  Time-series anomaly detection is a popular topic in both academia and
industrial fields. Many companies need to monitor thousands of temporal signals
for their applications and services and require instant feedback and alerts for
potential incidents in time. The task is challenging because of the complex
characteristics of time-series, which are messy, stochastic, and often without
proper labels. This prohibits training supervised models because of lack of
labels and a single model hardly fits different time series. In this paper, we
propose a solution to address these issues. We present an automated model
selection framework to automatically find the most suitable detection model
with proper parameters for the incoming data. The model selection layer is
extensible as it can be updated without too much effort when a new detector is
available to the service. Finally, we incorporate a customized tuning algorithm
to flexibly filter anomalies to meet customers' criteria. Experiments on
real-world datasets show the effectiveness of our solution.
