This article proposes a framework that provides early detection of anomalous series within a large collection of non-stationary streaming time series data.
We present a framework for automated anomaly detection in high-frequency water-quality data from in situ sensors, using turbidity, conductivity and river level data collected from rivers flowing into the Great Barrier Reef.