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 2022
Recurrent Neural Network Forecasting Framework with Data Augmentation
15/02/19 → 31/10/19
Project: Research
C4NET: Centre for New Energy Technologies
Oglesby, D., Brear, M., Evans, R. J., Mancarella, P., Jasieniak, J., Hyndman, R., Rivers, G. & Gordon, R.
Monash University – Internal Faculty Contribution, The University of Melbourne, Australian Energy Market Operator Ltd, Deakin University, Federation University Australia, RMIT University, Swinburne University of Technology
1/07/18 → 30/06/22
Project: Other
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 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
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
A note on the validity of cross-validation for evaluating autoregressive time series prediction
Bergmeir, C., Hyndman, R. J. & Koo, B., 1 Apr 2018, In : Computational Statistics and Data Analysis. 120, p. 70-83 14 p.Research output: Contribution to journal › Article › Research › peer-review
Bivariate smoothing of mortality surfaces with cohort and period ridges
Dokumentov, A., Hyndman, R. J. & Tickle, L., 1 Jan 2018, In : Stat. 7, 13 p., e199.Research output: Contribution to journal › Article › Research › peer-review
Crude oil price forecasting based on internet concern using an extreme learning machine
Wang, J., Athanasopoulos, G., Hyndman, R. J. & Wang, S., 1 Oct 2018, In : International Journal of Forecasting. 34, 4, p. 665-677 13 p.Research output: Contribution to journal › Article › Research › peer-review