State space models

Published on 29 May 2014 in Talks

A one-day workshop for the Australian Bureau of Statistics, 30 May 2014

1. Exponential smoothing
2. Structural models
3. ARIMA and RegARMA models, and dlm


Common functional principal component models for mortality forecasting

Rob J Hyndman and Farah Yasmeen

International Workshop on Functional and Operatorial Statistics, Stresa, Italy. 19-21 June 2014

Abstract: We explore models for forecasting groups of functional time series data that exploit common features in the data. Our models involve fitting common (or partially common) functional principal component models and forecasting the coefficients using univariate time series methods. We illustrate our approach by forecasting age-specific mortality rates for males and females in Australia.


Reconciling forecasts for hierarchical and grouped time series

Published on 24 May 2014 in Working papers

Rob J Hyndman and George Athanasopoulos This is an introduction to our approach to forecast reconciliation without using any matrices. The original research is available here: Hyndman, Ahmed, Athanasopoulos and Shang (CSDA, 2011) Athanasopoulos, Ahmed and Hyndman (IJF, 2009) The software is available in the hts package for R with some notes on usage in the vignette. There is also a gentle introduction in my forecasting textbook.


Monash Electricity Forecasting Model

Published on 22 May 2014 in Working papers

Rob J Hyndman and Shu Fan

The model we developed for peak electricity demand forecasting in Hyndman and Fan (2010) is now widely used in practice around Australia, and has undergone many improvements and developments. This document describes the current version of the model. It will be updated from time to time as the model continues to be modified and improved.


forecast package for R

Published on 8 May 2014 in Software

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. It also includes a handful of data sets from the Time Series Data Library. The package is described in Hyndman and Khandakar (2008).