Projects per year
Personal profile
Biography
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.
External positions
Director, International Institute of Forecasters
2005 → …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 2021
Advisian Digital, Proof of Concept for Application of Advanced Short Term Power Generation Forecasting Technology for Wind and Solar Farms
Nitchov, T., Marshment, D., Bergmeir, C., Brown, S., Danka, G., Vahid-Araghi, F., Miller, B., Hyndman, R., Ben Taieb, S., Salehi, M. & Khalilpour, K.
28/05/19 → 6/01/21
Project: Research
Recurrent Neural Network Forecasting Framework with Data Augmentation
15/02/19 → 31/10/19
Project: Research
RiskLab: RiskLab Projects - Econometrics
Hyndman, R., Cook, D. & Knight, K.
1/01/17 → 31/12/19
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, The University of Melbourne, Queensland University of Technology , The University of Adelaide, The University of New South Wales, The University of Queensland , University of Technology Sydney, Monash University – Internal University Contribution, Monash University – Internal Faculty Contribution, Monash University – Internal School Contribution, VicRoads
1/01/17 → 31/12/20
Project: Research
Research Output 1992 2019
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 journal › Article › Research › peer-review
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 journal › Article › Research › peer-review
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 journal › Article › Research › peer-review
Macroeconomic forecasting for Australia using a large number of predictors
Panagiotelis, A., Athanasopoulos, G., Hyndman, R. J., Jiang, B. & Vahid, F., 1 Apr 2019, In : International Journal of Forecasting. 35, 2, p. 616-633 18 p.Research output: Contribution to journal › Article › Research › peer-review
Optimal forecast reconciliation for hierarchical and grouped time series through trace minimization
Wickramasuriya, S. L., Athanasopoulos, G. & Hyndman, R. J., 2019, In : Journal of the American Statistical Association. 114, 526, p. 804-819 16 p.Research output: Contribution to journal › Article › Research › peer-review