Low-count Time Series Anomaly Detection
Philipp Renz Kurt Cutajar Niall Twomey Gavin K. C. Cheung Hanting Xie
Low-count time series describe sparse or intermittent events, which are
prevalent in large-scale online platforms that capture and monitor diverse data
types. Several distinct challenges surface when modelling low-count time
series, particularly low signal-to-noise ratios (when anomaly signatures are
provably undetectable), and non-uniform performance (when average metrics are
not representative of local behaviour). The time series anomaly detection
community currently lacks explicit tooling and processes to model and reliably
detect anomalies in these settings. We address this gap by introducing a novel
generative procedure for creating benchmark datasets comprising of low-count
time series with anomalous segments. Via a mixture of theoretical and empirical
analysis, our work explains how widely-used algorithms struggle with the
distribution overlap between normal and anomalous segments. In order to
mitigate this shortcoming, we then leverage our findings to demonstrate how
anomaly score smoothing consistently improves performance. The practical
utility of our analysis and recommendation is validated on a real-world dataset
containing sales data for retail stores.