Rob J Hyndman, George Athanasopoulos and Han Lin Shang The new version of the hts package (v3.01) has a vignette.
A gradient boosting approach to the Kaggle load forecasting competition
International Journal of Forecasting, to appear. Souhaib Ben Taieb (1) and Rob J Hyndman (2) (1) Machine Learning Group, Department of Computer Science, Université Libre de Bruxelles (2) Department of Econometrics & Business Statistics, Monash University, Clayton, Victoria, Australia Abstract : We describe and analyse the approach used by Team TinTin (Souhaib Ben Taieb and Rob J Hyndman) in the Load Forecasting track of the Kaggle Global Energy Forecasting Competition 2012. The competition involved a hierarchical load forecasting problem for a US utility with 20 geographical zones. The available data consisted of the hourly loads for the 20 zones and hourly temperatures from 11 weather stations, for four and a half
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Man vs wild data
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 familiar. I will describe some
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Coherent mortality forecasting: the product-ratio method with functional time series models
Rob J Hyndmana, Heather Boothb and Farah Yasmeena aDepartment of Econometrics & Business Statistics, Monash University, Clayton, Victoria, Australia. bThe Australian Demographic & Social Research Institute, Australian National University, Canberra, ACT, Australia. Demography, 50(1), 261–283. Revised version: 20 April 2012. Abstract: When independence is assumed, forecasts of mortality for subpopulations are almost always divergent in the long term. We propose a method for coherent forecasting of mortality rates for two or more subpopulations, based on functional principal components models of simple and interpretable functions of rates. The product-ratio functional forecasting method models and forecasts the geometric mean of subpopulation rates and the ratio
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SimpleR: tips, tricks and tools
Melbourne R Users’ Group Tuesday 20 November 2012 Deloitte, Level 11, 550 Bourke Street, Melbourne Slides and video on my blog.
Recursive and direct multi-step forecasting: the best of both worlds
Souhaib Ben Taieb1 and Rob J Hyndman2 Université Libre de Bruxelles Monash University Abstract: We propose a new forecasting strategy, called rectify, that seeks to combine the best properties of both the recursive and direct forecasting strategies. The rationale behind the rectify strategy is to begin with biased recursive forecasts and adjust them so they are unbiased and have smaller error. We use linear and nonlinear simulated time series to investigate the performance of the rectify strategy and compare the results with those from the recursive and the direct strategies. We also carry out some experiments using real world time series from
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A case-crossover design to examine the role of aeroallergens and respiratory viruses on childhood asthma exacerbations requiring hospitalisation: The MAPCAH study
Erbas B, Dharmage SC, O’Sullivan M, Akram M, Newbigin E, Taylor P, Vicendese D, Hyndman RJ, Tang ML, Abramson MJ. Journal of Biometrics and Biostatistics (2012), S7-018. Abstract Background: Few case-control studies of time dependent environmental exposures and respiratory outcomes have been performed. Small sample sizes pose modeling challenges for estimating interactions. In contrast, case cross-over studies are well suited where control selection and responses are low, time consuming and costly. Objective: To demonstrate the feasibility and validity of a case crossover study of children admitted to hospital for asthma to examine interacting effects of time varying environmental exposures. Methods: The
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Advances in automatic time series forecasting
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 developed for each series
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Short-term load forecasting based on a semi-parametric additive model
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 region
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Rob J Hyndman is
