Recent activity

26 May 2015:  
Visualization of big time series data
Talk given to a joint meeting of the Statistical Society of Australia (Victorian branch) and the Melbourne Data Science Meetup Group.
22 May 2015:  
Probabilistic forecasting of long-term peak electricity demand
The latest version of my talk on electricity demand forecasting. Given to the "Monash Energy Materials and Systems Institute"
20 Apr 2015:  
A note on the validity of cross-validation for evaluating time series prediction
By Christoph Bergmeir, Rob J Hyndman, and Bonsoo Koo
4 Apr 2015:  
Discussion of “High-dimensional autocovariance matrices and optimal linear prediction”
My discussion of the article on "High-dimensional autocovariance matrices and optimal linear prediction" by Timothy L. McMurry and Dimitris N. Politis. Electronic J Statistics (2015) 9, 792-796.
23 Feb 2015:  
Visualization and forecasting of big time series data
Talk given at the ACEMS Big data workshop, QUT.
12 Jan 2015:  
Visualizing and forecasting big time series data
Talk given at the Institute of Statistical Science, Academia Sinica, Taipei, Taiwan.
24 Dec 2014:  
Bivariate data with ridges: two-dimensional smoothing of mortality rates
By Alexander Dokumentov and Rob J Hyndman
17 Dec 2014:  
MEFM package for R
The MEFM package for R includes a set of tools for implementing the Monash Electricity Forecasting Model.
21 Oct 2014:  
Optimally reconciling forecasts in a hierarchy
By Rob J Hyndman and George Athanasopoulos Foresight (Fall, 2014). pp.42-48.
23 Sep 2014:  
Forecasting: principles and practice (UWA course)
Workshop held at UWA on 23-25 September 2014.
1 Sep 2014:  
Outdoor fungal spores are associated with child asthma hospitalisations - a case-crossover study
By Rachel Tham, Shyamali Dharmage, Philip Taylor, Ed Newbigin, Mimi L.K. Tang, Don Vicendese, Rob J Hyndman, Michael J Abramson and Bircan Erbas European Respiratory Journal (2014), 44(Suppl 58).
1 Aug 2014:  
Efficient identification of the Pareto optimal set
By Ingrida Steponavičė, Rob J Hyndman, Kate Smith-Miles and Laura Villanova Learning and Intelligent Optimization. Lecture Notes in Computer Science, vol 8426, 341-352.
1 Jul 2014:  
Fast computation of reconciled forecasts in hierarchical and grouped time series
Talk given at the International Symposium on Forecasting, Rotterdam.
24 Jun 2014:  
Functional time series with applications in demography
A short course given at Humboldt University, Berlin, 24-25 June 2014.
17 Jun 2014:  
Challenges in forecasting peak electricity demand
A two-part seminar given at the Energy Forum, Valais/Wallis, Switzerland.
5 Jun 2014:  
Low-dimensional decomposition, smoothing and forecasting of sparse functional data
By Alexander Dokumentov and Rob J Hyndman
5 Jun 2014:  
Fast computation of reconciled forecasts for hierarchical and grouped time series
By Rob J Hyndman, Ala Lee & Earo Wang
30 May 2014:  
State space models
A one-day workshop for the Australian Bureau of Statistics
24 May 2014:  
Common functional principal component models for mortality forecasting
By Rob J Hyndman and Farah Yasmeen Contributions in infinite-dimensional statistics and related topics. Chapter 29, pages 161-166.
22 May 2014:  
Monash Electricity Forecasting Model
By Rob J Hyndman and Shu Fan
8 May 2014:  
forecast package for R
The forecast package for R provides methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling.
2 May 2014:  
“Facts” may still be artefacts, since models can make unrealistic assumptions: statistical methods for the estimation of invasion lag-phases from herbarium data
By Rob J Hyndman, Mohsen B Mesgaran and Roger D Cousens
9 Apr 2014:  
hts package for R
The hts package provides methods for analysing and forecasting hierarchical time series.
1 Apr 2014:  
A gradient boosting approach to the Kaggle load forecasting competition
By Souhaib Ben Taieb and Rob J Hyndman International Journal of Forecasting (2014), 30(2), 382–394.
31 Mar 2014:  
Measuring forecast accuracy
This is a chapter for a new book on forecasting in business.