Rob J Hyndman is Professor of Statistics and Head of the Department of Econometrics and Business Statistics at Monash University. From 2005-2018 he was Editor-in-Chief of the International Journal of Forecasting and a Director of the International Institute of Forecasters. Rob is the author of over 150 research papers and books in statistical science. In 2007, he received the Moran medal from the Australian Academy of Science for his contributions to statistical research, especially in the area of statistical forecasting. For 30 years, Rob has maintained an active consulting practice, assisting hundreds of companies and organizations around the world. His recent consulting work has involved forecasting electricity demand, tourism demand, the Australian government health budget and case volume at a US call centre.
Water-quality sensors are exposed to changing environments and extreme weather conditions and thus are prone to errors, including failure. These technical errors make data unreliable and untrustworthy and affect performance of any subsequent data analysis. ACEMS researchers, led by Priyanga Dilini Talagala, have proposed a feature based procedure, named oddwater, for detecting technical outliers in water-quality data derived from in situ sensors.
The ability to detect anomalies or outliers in time series, and to develop algorithms that can find them at the earliest possible sign of a deviation from expected behaviour, is critical for many applications including those arising in security, epidemiology, and monitoring of critical infrastructure. This project aims to develop new methods to characterise the expected time series behaviour of systems, even in the presence of significant noise and concept drift, and with spatio-temporal and multivariate time series.
Anomalies can be the main carriers of significant and often critical information, and the identification of anomalies is a key task in many fields such as cyber security, intrusion detection, water quality monitoring, system health monitoring, environmental monitoring. ACEMS researchers, led by Priyanga Dilini Talagala, have proposed a framework that provides early detection of anomalous series within a large collection of streaming time-series data.
Steponavič\.e, I., Shirazi-Manesh M., Hyndman R. J., Smith-Miles K., Villanova L., Pardalos P.. M., et al.
(2016). On sampling methods for costly multi-objective black-box optimization. Advances in Stochastic and Deterministic Global Optimization. 273–296.
Invited talks, refereed proceedings and other conference outputs