Professor Rob Hyndman
Professor
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.
For more information please visit his Personal website
Research Interests:
Anomaly Detection
Forecasting
Time series modelling
Visualisation
Qualifications:
BSc (Hons), University of Melbourne, 1988
PhD, University of Melbourne, 1993
Projects
Publications
Books
Book Chapters
(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
(2021). Manifold Learning with Approximate Nearest Neighbours.
Early Career & Student Statisticians Conference 2021.
(2021). Uncertain Futures.
Australian Academy of Science.
(2021). Manifold Learning with Approximate Nearest Neighbours.
AMSI Winter School 2021.
(2021). Feasts & fables: modern tools for time series analysis.
Why R? 2021 Conference.
(2021). Probabilistic ensemble forecasting of Australian COVID-19 cases.
International Symposium on Forecasting 2021.
(2021). GRATIS: GeneRAting TIme Series with diverse and controllable characteristics.
ADSN Focused Workshop: Synthetic Data.
(2021). The geometry of forecast reconciliation.
Talk given at City University of Hong Kong.
(2020). Ensemble forecasts using fable.
R Conference 2020.
(2020). How Rmarkdown changed my life.
RStudio Conference 2020.
(2020). Ten years of forecast reconciliation.
International Symposium on Forecasting 2020.
(2020). The geometry of forecast reconciliation.
Macquarie University Department of Mathematics & Statistics Colloquium.
(2019). A feast of time series tools.
39th International Symposium on Forecasting.
(2019). Advancing forecasting research and practice.
39th International Symposium on Forecasting.
(2019). Quantification of energy savings from energy conservation measures in buildings using machine learning.
European Council for an Energy Efficient Economy Summer Study 2019.
(2019). Revealing high-frequency trading provision of liquidity with visualization.
2nd International Conference on Software Engineering and Information Management. pp. 157-165.
(2019). Forecasting is not prophecy: dealing with high-dimensional probabilistic forecasts in practice.
ISI World Statistics Conference 2019.
(2018). A classification framework for forecast-model selection.
Joint Statistical Meetings (JSM) 2018.
Journal Articles
(2022). STR: Seasonal-Trend decomposition using Regression.
INFORMS Journal on Data Science. 1(1), 50-62. doi: 10.1287/ijds.2021.0004
(2021). Understanding links between water-quality variables and nitrate concentration in freshwater streams using high-frequency sensor data.
arXiv. arXiv:2106.01719v1.
(2021). Monash Time Series Forecasting Archive.
arXiv. arXiv:2105.06643v1.
(2021). LoMEF: A Framework to Produce Local Explanations for Global Model Time Series Forecasts.
arXiv. arXiv:2111.07001v1.
(2021). MSTL: A Seasonal-Trend Decomposition Algorithm for Time Series with Multiple Seasonal Patterns.
arXiv. arXiv:2107.13462v1.
(2020). Distributed ARIMA Models for Ultra-long Time Series.
arXiv. arXiv:2007.09577v1,
(2020). Hierarchical forecast reconciliation with machine learning.
arXiv. arXiv:2006.02043v1,
(2015). Corrigendum to: “Hierarchical forecasts for Australian domestic tourism” [International Journal of Forecasting 25 (2009) 146–166].
International Journal of Forecasting. 31(2), 585. doi: 10.1016/j.ijforecast.2014.08.011
Publicly available softwares
(2019). fabletools: Core Tools for Packages in the 'fable' Framework.
(2019). feasts: Feature Extraction And Statistics for Time Series.
Technical reports and unrefereed outputs
(2022). Statistical and machine learning strategies for multi-bore forecasting for the Namoi Valley.
(2018). On normalization and algorithm selection for unsupervised outlier detection.
(2018). Anomaly Detection in Streaming Nonstationary Temporal Data.
