Unsupervised Anomaly Detection for Seasonal Time Series

摘要

We extend eBay's Atlas algorithm to automatically detect anomalies in unlabeled, seasonal time series data. Named MULDER, the algorithm involves deriving a "surprise" metric from the time series, which is then analysed statistically for anomalies. We evaluate the efficacy of MULDER via the Numenta Anomaly Benchmark, and calibrate it for deployment with injected anomalies on production data. We find that MULDER can be used to create alerts with a low false positive rate, and outperforms several popular open source implementations.