Rob J Hyndman The R journal Vol. 3/1, June 2011, p69-71. Abstract: Giving a useR! talk at the international R user conference is a balancing act in which you have to try to impart some new ideas, provide sufficient background and keep the audience interested, all in a very short period of time. Download paper
Evaluating extreme quantile forecasts
Talk to be given at the International Symposium on Forecasting, Prague, 26–29 June 2011. Slides
fpp package for R
The fpp package for R provides all data sets required for the examples and exercises in the book Forecasting: principles and practice by Rob J Hyndman and George Athanasopoulos. All packages required to run the examples are also loaded.
Tourism forecasting: an introduction
Haiyan Song and Rob J Hyndman International Journal of Forecasting (2011), 27. Introduction to the special issue on Tourism Forecasting. Online article
The price elasticity of electricity demand in South Australia
Shu Fan and Rob J Hyndman Business and Economic Forecasting Unit, Monash University, Clayton, Victoria 3800, Australia Energy policy (2011), 39(6), 3709–3719. Abstract In this paper, the price elasticity of electricity demand, representing the sensitivity of customer demand to the price of electricity, has been estimated for South Australia. We first undertake a review of the scholarly literature regarding electricity price elasticity for different regions and systems. Then we perform an empirical evaluation of the historic South Australian price elasticity, focussing on the relationship between price and demand quantiles at each half-hour of the day. This work attempts to determine whether there is
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Optimal combination forecasts for hierarchical time series
Rob J. Hyndman1, Roman A. Ahmed2, George Athanasopoulos1 and Han L Shang1 Department of Econometrics and Business Statistics, Monash University, VIC 3800, Australia. Department of Epidemiology and Preventive Medicine, Monash University, VIC, Australia. (Revised version: 10 September 2010) Computational Statistics and Data Analysis (2011), 55(9), 2579–2589. Abstract In many applications, there are multiple time series that are hierarchically organized and can be aggregated at several different levels in groups based on products, geography or some other features. We call these “hierarchical time series”. They are commonly forecast using either a “bottom-up” or a “top-down” method. In this paper we propose a new approach
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Improved interval estimation of long run response from a dynamic linear model: a highest density region approach
Jae H. Kim1 , Iain Fraser2 and Rob J. Hyndman1 Department of Econometrics and Business Statistics, Monash University, VIC 3800, Australia. University of Kent, UK. Computational Statistics and Data Analysis (2011), 55(8), 2477–2489. Abstract This paper proposes a new method of interval estimation for the long run response (or elasticity) parameter from a general linear dynamic model. We employ the bias-corrected bootstrap, in which small sample biases associated with the parameter estimators are adjusted in two stages of the bootstrap. As a means of bias-correction, we use alternative analytic and bootstrap methods. To take atypical properties of the long run elasticity estimator into account,
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Nonparametric time series forecasting with dynamic updating
Han Lin Shang and Rob J Hyndman Mathematics and Computers in Simulation (2011), 81, 1310–1324. Abstract We present a nonparametric method to forecast a seasonal univariate time series, and propose four dynamic updating methods to improve point forecast accuracy. Our methods consider a seasonal univariate time series as a functional time series. We propose first to reduce the dimensionality by applying functional principal component analysis to the historical observations, and then to use univariate time series forecasting and functional principal component regression techniques. When data in the most recent year are partially observed, we improve point forecast accuracy using dynamic updating methods. We also introduce
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The value of feedback in forecasting competitions
George Athanasopoulos and Rob J Hyndman Department of Econometrics & Business Statistics, Monash University, Australia. International Journal of Forecasting (2011), 27(3), 845–849. Abstract: In this paper we challenge the traditional design used for forecasting competitions. We implement an online competition with a public leaderboard that provides instant feedback to competitors who are allowed to revise and resubmit forecasts. The results show that feedback significantly improves forecasting accuracy.
My Bible material now at Musings
I’ve split off my Bible books, talks and articles to a separate page at robjhyndman.com/musings/ This rss feed will continue to provide updates on my statistical publications, talks and software. If you want my Bible talks and articles, head over to robjhyndman.com/musings/ and subscribe to the new feed.
Rob J Hyndman is Professor of Statistics at 