  We proposed a multivariate time series anomaly detection frame-work Ymir,
which leverages ensemble learning and supervisedlearning technology to
efficiently learn and adapt to anomaliesin real-world system applications. Ymir
integrates several currentlywidely used unsupervised anomaly detection models
through anensemble learning method, and thus can provide robust frontalanomaly
detection results in unsupervised scenarios. In a super-vised setting, domain
experts and system users discuss and providelabels (anomalous or not) for the
training data, which reflects theiranomaly detection criteria for the specific
system. Ymir leveragesthe aforementioned unsupervised methods to extract rich
and usefulfeature representations from the raw multivariate time series
data,then combines the features and labels with a supervised classifier todo
anomaly detection. We evaluated Ymir on internal multivariatetime series
datasets from large monitoring systems and achievedgood anomaly detection
performance.
