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We proposed a multivariate time series anomaly detection frame-work Ymir,which leverages ensemble learning and supervisedlearning technology toefficiently learn and adapt to anomaliesin real-world system applications. Ymirintegrates several currentlywidely used unsupervised anomaly detection modelsthrough anensemble learning method, and thus can provide robust frontalanomalydetection results in unsupervised scenarios. In a super-vised setting, domainexperts and system users discuss and providelabels (anomalous or not) for thetraining data, which reflects theiranomaly detection criteria for the specificsystem. Ymir leveragesthe aforementioned unsupervised methods to extract richand usefulfeature representations from the raw multivariate time seriesdata,then combines the features and labels with a supervised classifier todoanomaly detection. We evaluated Ymir on internal multivariatetime seriesdatasets from large monitoring systems and achievedgood anomaly detectionperformance.

Ymir: A Supervised Ensemble Framework for Multivariate Time Series Anomaly Detection