Professor Rob J Hyndman

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Forecasting electricity demand distributions using a semiparametric additive model

Published on 5 February 2012 in Talks

Talk to be given at the University of Adelaide, 3:10pm, Friday 16 March 2012 Abstract: Electricity demand forecasting plays an important role in short-​​​​term load allocation and long-​​​​term planning for future generation facilities and transmission augmentation. Planners must adopt a probabilistic view of potential peak demand levels, therefore density forecasts (providing estimates of the full probability distributions of the possible future values of the demand) are more helpful than point forecasts, and are necessary for utilities to evaluate and hedge the financial risk accrued by demand variability and forecasting uncertainty. Electricity demand in a given season is subject to a range of uncertainties, including

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Short-​​term load forecasting based on a semi-​​parametric additive model

Published on 1 February 2012 in Refereed papers

Shu Fan and Rob J Hyndman Revised 10 January 2011 IEEE Transactions on Power Systems (2012), 27(1), 134–141. Abstract Short-​​​​term load forecasting is an essential instrument in power system planning, operation and control. Many operating decisions are based on load forecasts, such as dispatch scheduling of generating capacity, reliability analysis, and maintenance planning for the generators. Overestimation of electricity demand will cause a conservative operation, which leads to the start-​​​​up of too many units or excessive energy purchase, thereby supplying an unnecessary level of reserve. On the contrary, underestimation may result in a risky operation, with insufficient preparation of spinning reserve, causing the system to operate in a vulnerable

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Forecasts of COPD mortality in Australia: 2006–2025

Published on 30 January 2012 in Refereed papers

Bircan Erbas, Shahid Ullah, Rob J Hyndman, Michelle Scollo, Michael Abramson

BMC Medical Research Methodology, 2012, to appear.

Chronic Obstructive Pulmonary Disease (COPD) is currently the fifth leading cause of death in Australia, and there are marked differences in mortality trends between men and women. In this study, we have sought to model and forecast age related changes in COPD mortality over time for men and women separately over the period 2006–2025.

 

Forecasting time series with complex seasonal patterns using exponential smoothing

Published on 31 December 2011 in Headline, Refereed papers

Alysha M De Livera, Rob J Hyndman and Ralph D Snyder Journal of the American Statistical Association (2011) 106(496), 1513–1527. Abstract A new innovations state space modeling framework, incorporating Box-​​​​Cox transformations, Fourier series with time varying coefficients and ARMA error correction, is introduced for forecasting complex seasonal time series that cannot be handled using existing forecasting models. Such complex time series include time series with multiple seasonal periods, high frequency seasonality, non-​​​​integer seasonality and dual-​​​​calendar effects. Our new modelling framework provides an alternative to existing exponential smoothing models, and is shown to have many advantages. The methods for initialization and estimation, including likelihood evaluation,

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Forecasting time series using R

Published on 27 October 2011 in Talks

Melbourne R Users’ Group Thursday, October 27, 2011, 6:00 PM Deloitte, Level 11 (Culture Room), 550 Bourke Street, Melbourne I will look at the various facilities for time series forecasting available in R, concentrating on the forecast package. This package implements several automatic methods for forecasting time series including forecasts from ARIMA models, ARFIMA models and exponential smoothing models. I will also look more generally at how to go about forecasting non-​​​​seasonal data, seasonal data, seasonal data with high frequency, and seasonal data with multiple frequencies. Examples will be taken from my own consulting experience. I will give an overview of what’s possible

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Forecasting electricity demand distributions using a semiparametric additive model

Published on 3 October 2011 in Talks

Talk to be given at the University of Melbourne at 1pm, Tuesday 11 October 2011. Location: Room 213, Richard Berry Building, University of Melbourne. Abstract: Electricity demand forecasting plays an important role in short-​​​​term load allocation and long-​​​​term planning for future generation facilities and transmission augmentation. Planners must adopt a probabilistic view of potential peak demand levels, therefore density forecasts (providing estimates of the full probability distributions of the possible future values of the demand) are more helpful than point forecasts, and are necessary for utilities to evaluate and hedge the financial risk accrued by demand variability and forecasting uncertainty. Electricity

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Investigating the influence of synoptic-​​scale circulation on air quality using self-​​organizing maps and generalized additive modelling

Published on 16 July 2011 in Refereed papers

John L Pearce, Jason Beringer, Neville Nicholls, Rob J Hyndman, Petteri Uotila, and Nigel J Tapper

Atmospheric Environment (2011), 45(1), 128–136.

The influence of synoptic-​​scale circulations on air quality is an area of increasing interest to air quality management in regards to future climate change. This study presents an analysis where the dominant synoptic ‘types’ over the region of Melbourne, Australia are determined and linked to regional air quality.

 

Point and interval forecasts of mortality rates and life expectancy: a comparison of ten principal component methods

Published on 15 July 2011 in Refereed papers

Han Lin Shang, Heather Booth and Rob J Hyndman

Demographic Research, 25(5), 173–214.

Using the age– and sex-​​specific data of 14 developed countries, we compare the point and interval forecast accuracy and bias of ten principal component methods for forecasting mortality rates and life expectancy.

 

Method for optimizing coating properties based on an evolutionary algorithm approach

Published on 14 July 2011 in Refereed papers

Davide Carta, Laura Villanova, Stefano Costacurta, Alessandro Patelli, Irene Poli, Simone Vezzu, Paolo Scopece, Fabio Lisi, Kate Smith-​​​​Miles, Rob J Hyndman, Anita J. Hill, and Paolo Falcaro Analytical Chemistry (2011), 83(16), 6373–6380. ABSTRACT: In industry as well as many areas of scientific research, data collected often contain a number of responses of interest for a chosen set of exploratory variables. Optimization of such multivariable multiresponse systems is a challenge well suited to genetic algorithms as global optimization tools. One such example is the optimization of coating surfaces with the required absolute and relative sensitivity for detecting analytes using devices such as sensor arrays. High-​​​​throughput

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Giving a useR! talk

Published on 22 June 2011 in Opinion

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

 
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