  Methods for unsupervised anomaly detection suffer from the fact that the data
is unlabeled, making it difficult to assess the optimality of detection
algorithms. Ensemble learning has shown exceptional results in classification
and clustering problems, but has not seen as much research in the context of
outlier detection. Existing methods focus on combining output scores of
individual detectors, but this leads to outputs that are not easily
interpretable. In this paper, we introduce a theoretical foundation for
combining individual detectors with Bayesian classifier combination. Not only
are posterior distributions easily interpreted as the probability distribution
of anomalies, but bias, variance, and individual error rates of detectors are
all easily obtained. Performance on real-world datasets shows high accuracy
across varied types of time series data.
