Projects per year
Personal profile
Biography
Rob J Hyndman FAA FASSA is a Professor of Statistics in the Department of Econometrics and Business Statistics.
His academic qualifications include a Bachelor of Science (Honours) and a PhD from the University of Melbourne. He is an accredited statistician with the Statistical Society of Australia.
He is an elected Fellow of both the Australian Academy of Science, and the Academy of the Social Sciences in Australia.
Rob has researched and consulted with a wide range of business, industry and government clients. His work has included forecasting COVID-19 cases, demand forecasting for the electricity industry, estimating life expectancy for the Australian indigenous population, and forecasting Australian tourism demand.
He is an elected fellow of the International Institute of Forecasters, an elected member of the International Statistical Institute, and a member of the International Association for Statistical Computing, Institute of Mathematical Statistics, and the Statistical Society of Australia. He was Theory & Methods Editor of the Australian & New Zealand Journal of Statistics (2001-2004), Editor-in-chief of the International Journal of Forecasting (2005-2018), Editor of the Journal of Statistical Software since 2011, and Executive Editor of the R Journal since 2023.
Rob has received several prestigious awards for his research including the 2007 Moran Medal from the Australian Academy of Science, and the 2021 Pitman Medal from the Statistical Society of Australia. He has also been a recipient of the 2022 Australian Awards for University Teaching for outstanding contributions to student learning, the 2021 Vice-Chancellor's Award for Innovation in Learning and Teaching, the HP Innovation Research Award (2010), the Vice Chancellor's Award for Postgraduate Supervision (2008) and numerous Dean's awards for research, teaching, innovation and external collaboration.
Rob's research interests include forecasting, time series analysis, computational statistics, anomaly detection, and exploratory data analysis. He has also supervised more than 30 PhD and Masters students, with current projects including energy analytics, data visualization, hierarchical forecasting, anomaly detection and time series forecasting.
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
External positions
Director, International Institute of Forecasters
2005 → 2018
Research area keywords
- Business Analytics
- Machine Learning
- Forecasting
- Demography
- Computational Statistics
- Time Series
- Anomaly Detection
- Exploratory Data Analysis
Collaborations and top research areas from the last five years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
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SETAR-Tree: Global model-based forecasting with Trees and Threshold Autoregressive Models
Schmidt, D., Koo, B. & Hyndman, R.
21/10/22 → 31/12/23
Project: Research
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ARC Training Centre in Optimisation Technologies, Integrated Methodologies, and Applications (OPTIMA)
Smith-Miles, K., Stuckey, P., Taylor, P. G., Ernst, A., Aickelin, U., Garcia De La Banda Garcia, M., Pearce, A., Wallace, M., Bondell, H., Hyndman, R., Alpcan, T., Thomas, D. A., Anjomshoa, H., Kirley, M. G., Tack, G., Costa, A., Fackrell, M., Zhang, L., Glazebrook, K., Branke, J., O'Sullivan, B., O'Shea, N., Cheah, A., Meehan, A., Wetenhall, P., Bowly, D., Bridge, J., Faka, S., Mareels, I., Coleman, R. A. & Crook, J.
Engie Services Australia Pty Limited
23/09/21 → 23/09/26
Project: Research
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RACE for 2030: Reliable Affordable Clean Energy for 2030 Cooperative Research Centre (RACE for 2030 CRC)
Beins, D., Liebman, A., Bahrani, B., Leroux, A., Kraal, D., Razzaghi, R., Strengers, Y., Goodwin, S., Pink, S., Rudolph, C., Raven, R., Bergmeir, C., Saunderson, J., Leslie, G., Gangadharan, L., Islam, A., Wang, L. C., Ward, M., Xiao, E., Wallace, M., Tack, G., Stuckey, P., Li, Y., Pan, S., de Nijs, F., Buckingham, E., Denis-Ryan, A., Ens, B., Dargaville, R., Chang, X., Yu, J., White, S., Zlatanov, N., Hyndman, R., Panagiotelis, A. & Athanasopoulos, G.
1/07/20 → 30/06/31
Project: Research
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Loss-based Bayesian Prediction
Martin, G., Frazier, D., Hyndman, R. & Maneesoonthorn, O.
19/06/20 → 18/06/24
Project: Research
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Distributed ARIMA models for ultra-long time series
Wang, X., Kang, Y., Hyndman, R. J. & Li, F., Jul 2023, In: International Journal of Forecasting. 39, 3, p. 1163-1184 22 p.Research output: Contribution to journal › Article › Research › peer-review
4 Citations (Scopus) -
Forecasting, causality and feedback
Hyndman, R. J., Apr 2023, In: International Journal of Forecasting. 39, 2, p. 558-560 3 p.Research output: Contribution to journal › Comment / Debate › Other › peer-review
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Forecasting COVID-19 activity in Australia to support pandemic response: May to October 2020
Moss, R., Price, D. J., Golding, N., Dawson, P., McVernon, J., Hyndman, R. J., Shearer, F. M. & McCaw, J. M., 2023, In: Scientific Reports. 13, 1, 16 p., 8763.Research output: Contribution to journal › Article › Research › peer-review
Open AccessFile1 Citation (Scopus) -
Lee–Carter models: the wider context
Hyndman, R. J., Jul 2023, In: International Journal of Forecasting. 39, 3, p. 1053-1054 2 p.Research output: Contribution to journal › Comment / Debate › Other › peer-review
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Meta-learning how to forecast time series
Talagala, T. S., Hyndman, R. J. & Athanasopoulos, G., 2023, (Accepted/In press) In: Journal of Forecasting. 26 p.Research output: Contribution to journal › Article › Research › peer-review
1 Citation (Scopus)