### 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.