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Personal profile


Rob J Hyndman is a Professor of Statistics and Head of 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.

Rob has researched and consulted with a wide range of business, industry and government clients. His most recent work includes demand forecasting for the electricity industry, estimating life expectancy for the Australian indigenous population, and forecasting product demand for Huawei, the Chinese telecommunications company.

He is an elected member of the International Statistical Institute and a member of the International Institute of Forecasters, International Association for Statistical Computing, Institute of Mathematical Statistics, and the Statistical Society of Australia. He was editor-in-chief of International Journal of Forecasting from 2005-2018. 

Rob has received several awards for his research including the 2007 Moran Medal from the Australian Academy of Science. He has also been a recipient of the Dean's Award for excellence in innovation and external collaboration (2010), the HP Innovation Research Award (2010), the Vice Chancellor's Award for Postgraduate Supervision (2008) and the Dean's award for excellence in research (2008).

Rob's research interests include forecasting, time series analysis, computational statistics, and exploratory data analysis. He has also supervised more than 25 PhD and Masters students, with current projects including energy analytics, data visualization, hierarchical forecasting, anomaly detection and time series forecasting. 

Rob Hyndman's personal website

External positions

Director, International Institute of Forecasters

2005 → …

Research area keywords

  • Business Analytics
  • Machine Learning
  • Forecasting
  • Demography
  • Computational Statistics
  • Time Series

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Projects 2000 2022

Revolutionising water-quality monitoring in the information age

Mengersen, K., Hyndman, R., Peterson, E., McGree, J., Turner, R., Maxwell, P., Liquet, B. & Jones, J.


Project: Research

ARC Centre of Excellence for Mathematical and Statistical Frontiers of Big Data, Big Models, New Insights

Hall, P., Bartlett, P., Bean, N., Burrage, K., DeGier, J., Delaigle, A., Forrester, P., Geweke, J., Kohn, R., Kroese, D., Mengersen, K., Pettit, A., Pollett, P., Roughan, M., Ryan, L., Taylor, P., Turner, I., Wand, M., Garoni, T., Smith-Miles, K. A., Caley, M., Churches, T., Elazar, D., Gupta, A., Harch, B., Tam, S., Weegberg, K., Willinger, W. & Hyndman, R.

Australian Research Council (ARC), Monash University – Internal Department Contribution, University of Melbourne, Queensland University of Technology , University of Adelaide, University of New South Wales, University of Queensland , University of Technology Sydney, Monash University – Internal University Contribution, Monash University – Internal Faculty Contribution, Monash University – Internal School Contribution, VicRoads


Project: Research

Upgrade of investment risk analyser

Hyndman, R.

29/01/03 → …

Project: Research

Research Output 1992 2020

Hierarchical Forecasting

Athanasopoulos, G., Gamakumara, P., Panagiotelis, A., Hyndman, R. J. & Affan, M., 2020, Macroeconomic Forecasting in the Era of Big Data: Theory and Practice. Fuleky, P. (ed.). 1st ed. Cham Switzerland: Springer, p. 689-719 31 p. (Advanced Studies in Theoretical and Applied Econometrics; vol. 52).

Research output: Chapter in Book/Report/Conference proceedingChapter (Book)Researchpeer-review

6 Citations (Scopus)

A brief history of forecasting competitions

Hyndman, R. J., Jun 2019, (Accepted/In press) In : International Journal of Forecasting. 8 p.

Research output: Contribution to journalArticleResearchpeer-review

A feature-based procedure for detecting technical outliers in water-quality data from in situ sensors

Talagala, P. D., Hyndman, R. J., Leigh, C., Mengersen, K. & Smith-Miles, K., 2019, (Accepted/In press) In : Water Resources Research. 22 p.

Research output: Contribution to journalArticleResearchpeer-review

8 Citations (Scopus)

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

Leigh, C., Alsibai, O., Hyndman, R. J., Kandanaarachchi, S., King, O. C., McGree, J. M., Neelamraju, C., Strauss, J., Talagala, P. D., Turner, R. D. R., Mengersen, K. & Peterson, E. E., 10 May 2019, In : Science of the Total Environment. 664, p. 885-898 14 p.

Research output: Contribution to journalArticleResearchpeer-review

3 Citations (Scopus)

Anomaly detection in streaming nonstationary temporal data

Talagala, P. D., Hyndman, R. J., Smith-Miles, K., Kandanaarachchi, S. & Muñoz, M. A., 2019, (Accepted/In press) In : Journal of Computational and Graphical Statistics. 15 p.

Research output: Contribution to journalArticleResearchpeer-review