This paper develops a method for detecting technical outliers in water-quality data derived from *in situ* sensors.
The algorithm, stray, which is specially designed for high-dimensional data, addresses the limitations of the state-of-art-method, the HDoutliers algorithm.
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.
CRAN Task View: Anomaly Detection with R.
oddstream {Outlier Detection in Data STREAMs}
oddwater{Outlier Detection in Data from WATER-quality sensors}
stray {Search and TRace AnomalY}