Rob J Hyndman

Keynote address. Young Statisticians Conference 2013. Abstract: For 25 years I have been an intrepid statistical consultant, tackling the wild frontiers of real data, real problems and real time constraints. I have faced problems ranging from linguistics to river beds, from making paper plates to selling pies at the MCG, from tax office audits to surveys about the colour purple. University education helps prepare you to be a statistical consultant in the same way that Google maps helps prepare you to cross the Simpson Desert. You have some idea of the main features, but when you get there, nothing looks

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Melbourne R Users’ Group Tuesday 20 November 2012 Deloitte, Level 11, 550 Bourke Street, Melbourne Slides and video on my blog.

Invited talk, Australian Statistical Conference, Adelaide, 10 July 2012. COMPSTAT 2012, Cyprus, 29 August 2012. Seminar, Lancaster University, 10 September 2012. Abstract: Many applications require a large number of time series to be forecast completely automatically. For example, manufacturing companies often require weekly forecasts of demand for thousands of products at dozens of locations in order to plan distribution and maintain suitable inventory stocks. In population forecasting, there are often a few hundred time series to be forecast, representing various components that make up the population dynamics. In these circumstances, it is not feasible for time series models to be

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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

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Talk given at University of Melbourne, 11 October 2011. University of Adelaide, 16 March 2012 Monash University, 16 May 2012 La Trobe University, 24 May 2012 EDF, Paris. 4 September 2012 University of New South Wales, 1 November 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

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