Selected Publications

This article proposes a framework that provides early detection of anomalous series within a large collection of non-stationary streaming time series data.
In Journal of Computational and Graphical Statistics, 2019.

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.
In Science of The Total Environment, 2019.

Recent Publications

Anomaly Detection in Streaming Nonstationary Temporal Data

Details PDF Project Custom Link

A framework for automated anomaly detection in high frequency water-quality data from in situ sensors

Details PDF Custom Link

Recent & Upcoming Talks

More Talks


oddstream - R package

oddstream {Outlier Detection in Data STREAMs}

oddwater - R package

oddwater: Outlier Detection in Data from WATER-quality sensors

staplr - R package

A package containing a toolkit for PDF files

stray - R package

stray {STReam AnomalY} : Robust Anomaly Detection in Data Streams with Concept Drift