A gradient boosting approach to the Kaggle load forecasting competition

International Journal of Forecasting (2014), 30(2), 382–394. 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

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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

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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

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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

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Forecasts of COPD mortality in Australia: 2006-2025

Bircan Erbas1, Shahid Ullah2, Rob J Hyndman3, Michelle Scollo4, Michael Abramson5 BMC Medical Research Methodology (2012) 12:17. School of Public Health, La Trobe University, Bundoora, 3086Australia School of Human Movement and Sport Sciences, University of Ballarat,Mount Helen, Victoria, 3353,Australia Department of Econometrics and Business Statistics, Monash University, Clayton, 3800, Australia

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Forecasting time series with complex seasonal patterns using exponential smoothing

[latexpage] 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

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

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

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

Han Lin Shang1, Heather Booth2 and Rob J Hyndman1 Department of Econometrics & Business Statistics, Monash University, Clayton, Australia The Australian Demographic & Social Research Institute, Australian National University, Canberra, Australia. Demographic Research (2011), 25(5), 173-214. Revised: 5 April 2011 Abstract: Using the age- and sex-specific data of 14 developed

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Method for optimizing coating properties based on an evolutionary algorithm approach

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

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Hierarchy3

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,

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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)

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kaggle

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

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Quantifying the influence of local meteorology on air quality using generalized additive modelling

John L Pearcea, Jason Beringera, Neville Nichollsa, Rob J Hyndmanb and Nigel J Tappera a School of Geography and Environmental Science, Monash University, Melbourne, Australia b Department of Econometrics and Business Statistics, Monash University, Melbourne, Australia Atmospheric Environment (2011), 45(6), 1328-1336. Abstract Quantifying the observed relationships between local meteorology and air

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vists

The vector innovations structural time series framework: a simple approach to multivariate forecasting

Ashton de Silva1, Rob J Hyndman2 and Ralph D Snyder2 Statistical modelling (2010), vol. 10, no. 4, 353-374 School of Economics, Finance and Marketing, RMIT, VIC 3000, Australia. Department of Econometrics and Business Statistics, Monash University, VIC 3800, Australia. Abstract The vector innovations structural time series framework is proposed as

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Phenological change detection while accounting for abrupt and gradual trends in satellite image time series

Jan Verbesselt1, Rob J Hyndman2, Achim Zeilis3, Darius Culvenor1 Remote sensing team, CSIRO Sustainable Ecosystems, Private Bag 10, Melbourne VIC 3169, Australia Department of Econometrics and Business Statistics, Monash University, Melbourne VIC 3800, Australia Institute for Statistics, Leopold-Franzens-Universitt Innsbruck, 6020 Innsbruck, Austria Remote Sensing of Environment, 114(12), 2970-2980. Abstract A challenge in phenology studies is understanding

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Forecasting age-related changes in breast cancer mortality among white and black US women

Farah Yasmeen, Rob J Hyndman and Bircan Erbas Cancer Epidemiology, 34(5), 542-549. Abstract: The disparity in breast cancer mortality rates among white and black US women is widening, with higher mortality rates among black women. We apply functional time series models on age-specific breast cancer mortality rates for each group

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Using functional data analysis models to estimate future time trends of age-specific breast cancer mortality for the United States and England-Wales

Bircan Erbas1, Muhammad Akram2, Dorota M Gertig3, Dallas English4,5, John L. Hopper5, Anne M Kavanagh6 and Rob J Hyndman2 Journal of Epidemiology (2010), 20(2), 159-165. School of Public Health, La Trobe University, Bundoora, 3086 Australia Business and Economic Forecasting Unit, Monash University, Clayton, 3800, Australia. Victoria Cytology Service Inc, Carlton,

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Detecting trend and seasonal changes in satellite image time series

Jan Verbesselt1, Rob J Hyndman2, Glenn Newnham1, Darius Culvenor1 Remote Sensing of Environment (2010), 114(1), 106-115. Remote sensing team, CSIRO Sustainable Ecosystems, Private Bag 10, Melbourne VIC 3169, Australia Department of Econometrics and Business Statistics, Monash University, Melbourne VIC 3800, Australia Abstract A wealth of remotely sensed time series covering

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Density forecasting for long-term peak electricity demand

Rob J Hyndman and Shu Fan IEEE Transactions on Power Systems, 2010, 25(2), 1142-1153 Abstract: Long-term electricity demand forecasting plays an important role in planning for future generation facilities and transmission augmentation. In a long term context, planners must adopt a probabilistic view of potential peak demand levels, therefore density

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Exponential smoothing and non-negative data

Md. Akram1, Rob J. Hyndman1 and J. Keith Ord2 Australian and New Zealand Journal of Statistics (2009), 51(4), 415-432. Department of Econometrics and Business Statistics, Monash University, VIC 3800, Australia. McDonough School of Business, Georgetown University, Washington, DC20057, USA. Abstract The most common forecasting methods in business are based on

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Monitoring processes with changing variances

J. Keith Ord, Anne B. Koehler, Ralph D. Snyder and Rob J. Hyndman, International Journal of Forecasting (2009), 25(3), 518-525.Abstract: Statistical process control (SPC) has evolved beyond its classical applications in manufacturing to monitoring economic and social phenomena. This extension requires consideration of autocorrelated and possibly non-stationary time series. Less

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Rule induction for forecasting method selection: meta-learning the characteristics of univariate time series

Xiaozhe Wang1, Kate A. Smith-Miles1 and Rob J. Hyndman2 Neurocomputing, 72 (2009), 2581–2594. Faculty of Information Technology, Monash University, Clayton VIC 3800, Australia. Department of Econometrics and Business Statistics, Monash University, VIC 3800, Australia. Abstract This paper proposes a new method of interval estimation for the long run response (or

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Hierarchical forecasts for Australian domestic tourism

George Athanasopoulos1 , Roman A. Ahmed1 and Rob J. Hyndman1 International Journal of Forecasting (2009), 25(1), 146-166. Department of Econometrics and Business Statistics, Monash University, VIC 3800, Australia. Abstract In this paper we explore the hierarchical nature of tourism demand time series and produce short-term forecasts for Australian domestic tourism.

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A multivariate innovations state space Beveridge-Nelson decomposition

Economic modelling (2009), 26(5), 1067-1074 Ashton de Silva, Rob J Hyndman and Ralph Snyder Abstract The Beveridge-Nelson vector innovations structural time series framework is a new formulation that decomposes a set of variables into their permanent and transitory components. The proposed framework is flexible, modelling inter-series relationships and common features

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Forecasting time series with multiple seasonal patterns

European Journal of Operational Research (2008), 191(1), 207–220 Phillip G. Gould1, Anne B. Koehler2, Keith Ord3, Ralph D. Snyder1, Rob J. Hyndman11 and Farshid Vahid-Araghi4 Department of Econometrics and Business Statistics, Monash University, Clayton VIC 3800, Australia. Department of Decision Sciences and Management Information Systems, Miami University, Oxford, OH 45056,

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Stochastic population forecasts using functional data models for mortality, fertility and migration

International Journal of Forecasting (2008), 24(3), 323-342. Rob J Hyndman1 and Heather Booth2 Department of Econometrics and Business Statistics, Monash University, Clayton VIC 3800, Australia. Demography & Sociology Program, Research School of Social Sciences, The Australian National University, Canberra ACT 0200. Abstract Age-sex-specific population forecasts are derived through stochastic population

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Automatic time series forecasting: the forecast package for R

Journal of Statistical Software (2008), 27(3) Rob J. Hyndman and Yeasmin Khandakar Abstract: Automatic forecasts of large numbers of univariate time series are often needed in business and other contexts. We describe two automatic forecasting algorithms that have been implemented in the forecast package for R. The first is based

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The admissible parameter space for exponential smoothing models

Annals of the Institute of Statistical Mathematics (2008), 60(2), 407-426. Rob J. Hyndman1, Md. Akram1 and Blyth Archibald2 Department of Econometrics and Business Statistics, Monash University, Clayton VIC 3800, Australia. School of Business Administration, Dalhousie University, Halifax, Nova Scotia, Canada B3H 1Z5. Abstract: We discuss the admissible parameter space for

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Generation of synthetic sequences of half-hourly temperatures

Environmetrics (2008). 19(8), 818-835 Luciana Magnano1, John W. Boland1 and Rob J. Hyndman2 Centre for Industrial and Applied Mathematics, University of South Australia, SA 5095, Australia. Department of Econometrics and Business Statistics, Monash University, Clayton VIC 3800, Australia. Abstract: We present tools to generate synthetic sequences of half-hourly temperatures with

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Measurement of changes in antihypertensive drug utilization following primary care educational interventions

Pharmacoepidemiology & Drug Safety (2007), 16(3), 297-308. Fiona E. Horn1, John A. Mandryk1, Judith M. Mackson1, Sonia E. Wutzke1, Lynn M. Weekes1 and Rob J. Hyndman2 National Prescribing Service Ltd, Surry Hills, Sydney, Australia. Department of Econometrics and Business Statistics, Monash University, Clayton VIC 3800, Australia. Abstract: Purpose To measure

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Do levels of airborne grass pollen influence asthma hospital admissions?

Clinical and Experimental Allergy (2007), 37(11), 1641-1647. Bircan Erbas1, Jiun-Horng Chang1, Shyamali Dharmage1, Eng Kok Ong2, Rob J Hyndman3, Ed Newbigin4 and Michael Abramson5 Centre for Molecular Environmental Genetic Analytic Epidemiology School of Population Health, University of Melbourne Carlton, 3053, Victoria, Australia. Museum Victoria, GPO Box 666 Melbourne, 3001, Australia.

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Forecasting age-specific breast cancer mortality using functional data models

Statistics in Medicine (2007), 26(2), 458-470. Bircan Erbas1, Rob J Hyndman2 and Dorota M. Gertig3 Department of Public Health, The University of Melbourne, VIC 3053, Australia. Department of Econometrics and Business Statistics, Monash University, Clayton VIC 3800, Australia. Centre for Genetic Epidemiology, The University of Melbourne, VIC 3053, Australia. Abstract:

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Another look at measures of forecast accuracy

International Journal of Forecasting (2006). 22(4), 679-688. Rob J. Hyndman1 and Anne B. Koehler2 Department of Econometrics and Business Statistics, Monash University, VIC 3800, Australia. Department of Decision Sciences and Management Information Systems, Miami University, Oxford, OH 45056, USA. Abstract: We discuss and compare measures of accuracy of univariate time

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Lee-Carter mortality forecasting: a multi-country comparison of variants and extensions

Demographic Research (2006), 15(9), 289-310. Heather Booth1, Rob J. Hyndman2, Leonie Tickle3 and Piet De Jong3 Demography and Sociology Program, Australian National University, Canberra ACT 0200, Australia. Department of Econometrics and Business Statistics, Monash University, VIC 3800, Australia. Department of Actuarial Studies, Macquarie University, NSW 2109, Australia. Abstract: We compare

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A Bayesian approach to bandwidth selection for multivariate kernel density estimation

Computational Statistics & Data Analysis (2006), 50(11), 3009-3031. Xibin Zhang, Maxwell L King and Rob J. Hyndman Abstract: Kernel density estimation for multivariate data is an important technique that has a wide range of applications. However, it has received significantly less attention than its univariate counterpart. The lower level of

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25 years of time series forecasting

International Journal of Forecasting (2006), 22(3), 443-473. Jan G De Gooijer1 and Rob J Hyndman2 Department of Quantitative Economics, University of Amsterdam, Roetersstraat 11, 1018 WB Amsterdam, The Netherlands. Department of Econometrics and Business Statistics, Monash University, Clayton VIC 3800, Australia. Abstract We review the past 25 years of research

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Characteristic-based clustering for time series data

Data Mining and Knowledge Discovery, 13(3), 335-364. Xiaozhe Wang1, Kate A. Smith1 and Rob J. Hyndman2 Faculty of Information Technology, Monash University, Clayton VIC 3800, Australia. Department of Econometrics and Business Statistics, Monash University, VIC 3800, Australia. Abstract: With the growing importance of time series clustering research, particularly for similarity

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The accuracy of television network rating forecasts: the effects of data aggregation and alternative models

Model assisted statistics and applications (2006), 1(3), 147-155. Denny Meyer1 and Rob J. Hyndman2 Faculty of Life and Social Sciences, Swinburne University of Technology, Hawthorn VIC 3122. Department of Econometrics and Business Statistics, Monash University, VIC 3800, Australia. Abstract: This paper investigates the effect of aggregation in relation to the

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Sensitivity of the estimated air pollution-respiratory admissions relationship to statistical model

International Journal of Environmental Health Research (2005), 15(6), 437-448. Bircan Erbas1 and Rob J Hyndman2 Department of Public Health, The University of Melbourne, VIC 3010, Australia. Department of Econometrics and Business Statistics, Monash University, Clayton VIC 3800, Australia. Abstract: Study objective: The objective of this study is to demonstrate the

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Empirical information criteria for time series forecasting model selection

Journal of Statistical Computation and Simulation (2005), 75(10), 831-840. Baki Billah1, Rob J. Hyndman1 and Anne B. Koehler2 Department of Econometrics and Business Statistics, Monash University, VIC 3800, Australia. Department of Decision Sciences and Management Information Systems, Miami University, Oxford, OH 45056, USA. Abstract: In this paper, we propose a

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Prediction intervals for exponential smoothing using two new classes of state space models

Journal of Forecasting (2005), 24(1), 17-37. Rob J. Hyndman1, Anne B. Koehler2, J. Keith Ord3 and Ralph D. Snyder1 Department of Econometrics and Business Statistics, Monash University, VIC 3800, Australia. Department of Decision Sciences and Management Information Systems, Miami University, Oxford, OH 45056, USA. 320 Old North, Georgetown University, Washington,

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Nonparametric confidence intervals for receiver operating characteristic curves

Biometrika (2004), 91(3), 743-750. Peter Hall1, Rob J. Hyndman2 and Yanan Fan3 Centre for Mathematics and its Applications, Australian National University, Canberra ACT 0200, Australia. Department of Econometrics and Business Statistics, Monash University, VIC 3800, Australia. Department of Mathematics and Computer Science, University of Puerto Rico, PO Box 23355, San

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Exponential smoothing models: Means and variances for lead-time demand

European Journal of Operational Research (2004), 158(2) 444-455. Ralph D. Snyder1, Anne B. Koehler2, Rob J. Hyndman1 and J. Keith Ord3 Department of Econometrics and Business Statistics, Monash University, VIC 3800, Australia. Department of Decision Sciences and Management Information Systems, Miami University, Oxford, OH 45056, USA. 320 Old North, Georgetown

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Spline interpolation for demographic variables: the monotonicity problem

Journal of Population Research (2004), 21(1), 95-98. Len Smith1, Rob J. Hyndman2 and Simon N. Wood3 Australian Centre for Population Research, Australian National University, ACT 0200, Australia. Department of Econometrics and Business Statistics, Monash University, VIC 3800, Australia. Department of Statistics, The University of Glasgow, Glasgow G12 8QQ, U. K.

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Normative data for the Test of Visual Analysis Skills on an Australian population

Optometry and Vision Science (2003), 80(6), 431-436. Francoise Rateau1, Bastien Laumonier1 and Rob J. Hyndman2 Howell, Croucher, Rateau & Associates, Heathmont, Australia. Department of Econometrics and Business Statistics, Monash University, VIC 3800, Australia. Abstract: Purpose: The purpose of this study was to produce normative data for Rosner's Test of Visual

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Improved methods for bandwidth selection when estimating ROC curves

Statistics and Probability Letters (2003), 64 181-189. Peter G. Hall1 and Rob J. Hyndman1,2 Centre for Mathematics and its Applications, Australian National University, Canberra ACT 0200, Australia. Department of Econometrics and Business Statistics, Monash University, VIC 3800, Australia. Abstract: The receiver operating characteristic (ROC) curve is used to describe the

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Mixed model-based hazard estimation

Journal of Computational and Graphical Statistics (2002), 11(4), 784-798. T. Cai1, R.J. Hyndman2 and M.P. Wand3 Department of Biostatistics, University of Washington, Seattle, Washington 98195, USA. Department of Econometrics and Business Statistics, Monash University, VIC 3800, Australia. Department of Biostatistics, Harvard University, Boston, Massachusetts 02115, USA. Abstract: We propose a

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Nonparametric estimation and symmetry tests for conditional density functions

Journal of Nonparametric Statistics (2002), 14(3), 259-278. Rob J Hyndman1 and  Qiwei Yao2 Department of Econometrics and Business Statistics, Monash University, Clayton VIC 3800, Australia. Department of Statistics, London School of Economics Houghton Street, London WC2A 2AE, U.K. Abstract: We suggest two new methods for conditional density estimation. The first is

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A state space framework for automatic forecasting using exponential smoothing methods

International Journal of Forecasting (2002), 18(3), 439-454. Rob J. Hyndman1,Anne B. Koehler2,Ralph D. Snyder1 and Simone Grose1 Department of Econometrics and Business Statistics, Monash University, VIC 3800, Australia. Department of Decision Sciences and Management Information Systems, Miami University, Oxford, OH 45056, USA. Abstract: We provide a new approach to automatic

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Cycles and synchrony in the Collared Lemming (Dicrostonyx groenlandicus) in Arctic North America

Martin Predavec, Charles Kreb,  Kajell Danell and Rob J Hyndman Oecologia (2001). 126, 216-224. Abstract: Lemming populations are generally characterised by their cyclic nature, yet empirical data to support this are lacking for most species, largely because of the time and expense necessary to collect long-term population data. In this

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Data visualization for time series in environmental epidemiology

Journal of Epidemiology and Biostatistics (2001), 6(6), 433-443. Bircan Erbas1 and Rob J Hyndman2 Department of General Practice & Public Health, The University of Melbourne, VIC 3010, Australia. Department of Econometrics and Business Statistics, Monash University, VIC 3800, Australia. Abstract: Data visualization has become an integral part of statistical modelling.

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Bandwidth selection for kernel conditional density estimation

[latexpage] Computational Statistics and Data Analysis (2001), 36(3), 279-298. David Bashtannyk and Rob J Hyndman Abstract: We consider bandwidth selection for the kernel estimator of conditional density with one explanatory variable. Several bandwidth selection methods are derived ranging from fast rules-of-thumb which assume the underlying densities are known to relatively

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Residual diagnostic plots for model mis-specification in time series regression

Australian and New Zealand Journal of Statistics (2000), 42(4), 463-477. Richard Fraccaro1,2, Rob J Hyndman1 and Alan Veevers2 Department of Econometrics and Business Statistics, Monash University, Clayton VIC 3800, Australia. CSIRO Mathematical and Information Sciences, Clayton VIC 3168, Australia. Abstract: This paper considers residuals for time series regression. Despite much

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Generalized additive modelling of mixed distribution Markov models with application to Melbourne’s rainfall

Australian and New Zealand Journal of Statistics (2000), 42(2), 145-158. Rob J Hyndman1 and Gary K Grunwald2 Department of Econometrics and Business Statistics, Monash University, Clayton VIC 3800, Australia. Department of Preventive Medicine and Biometrics, Box B-119, University of Colorado Health Sciences Center, Denver, CO 80262, USA. Abstract: We consider

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Smoothing non-Gaussian time series with autoregressive structure

Computational Statistics and Data Analysis (1998), 28, 171-191. Gary Grunwald1 and Rob J Hyndman2 Center for Human Nutrition, Box C225, University of Colorado Health Sciences Center, 4200 E 9th Ave, Denver, CO 80262, USA. Department of Econometrics and Business Statistics, Monash University, Clayton VIC 3168, Australia. Abstract: We consider nonparametric

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Nonparametric autocovariance function estimation

Australian Journal of Statistics (1997), 39, 313-325. Rob J Hyndman1 and Matt Wand2 Department of Econometrics and Business Statistics, Monash University, Clayton VIC 3168, Australia. Harvard School of Public Health, Harvard University, Massachusetts, USA. Abstract: Nonparametric estimators of autocovariance functions for non-stationary time series are developed. The estimators are based

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The pricing and trading of options using a hybrid neural network model with historical volatility

NeuroVe$t Journal (1997), 5(1), 27-41. (Later known as Journal of Computational Intelligence in Finance) Paul Lajbcygier, Andrew Flitman, Anthony Swan and Rob J. Hyndman Abstract: The residuals between conventional option pricing models and market prices have persistent patterns or biases. The "hybrid" method models the residuals using an artificial neural

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Sample quantiles in statistical packages

American Statistician (1996), 50 361-365. Rob J Hyndman1 and Yanan Fan2 Department of Econometrics and Business Statistics, Monash University, Clayton VIC 3800, Australia. School of Mathematics and Statistics, University of NSW. Abstract: There are a large number of different definitions used for sample quantiles in statistical computer packages. Often within

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Estimating and visualizing conditional densities

Journal of Computational and Graphical Statistics (1996), 5 315-336. Rob J Hyndman1 and David Bashtannyk1 Abstract: We consider the kernel estimator of conditional density and derive its asymptotic bias, variance and mean-square error. Optimal bandwidths (with respect to integrated mean-square error) are found and it is shown that the convergence

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Computing and graphing highest density regions

American Statistician (1996),50, 120-126. Rob J Hyndman1 Abstract: Many statistical methods involve summarizing a probability distribution by a region of the sample space covering a specified probability. One method of selecting such a region is to require it to contain points of relatively high density. Highest density regions are particularly

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Highest density forecast regions for non-linear and non-normal time series models

Journal of Forecasting (1995),14, 431-441. Rob J Hyndman Abstract: Many modern time series methods, such as those involving non-linear models or non-normal data, frequently lead to forecast densities which are asymmetric or multi-modal. The problem of obtaining forecast regions in such cases is discussed and it is proposed that highest

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Approximations and boundary conditions for continuous time threshold autoregressive processes

Journal of Applied Probability (1994), 31(4), 1103-1109. Rob J. Hyndman1 Department of Statistics, The University of Melbourne, VIC 3052, Australia Abstract: Continuous time threshold autoregressive (CTAR) processes have been developed in the past few years for modelling non-linear time series observed at irregular intervals. Several approximating processes are given here

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On continuous-time threshold autoregression

International Journal of Forecasting (1992), 18(3), 439-454. P. J. Brockwell1 and R. J. Hyndman2 Statistics Department, Colorado State University, Fort Collins, CO 80523, USA. Statistics Department, University of Melbourne, Parkville, Vic. 3052, Australia. Abstract: The use of non-linear models in time series analysis has expanded rapidly in the last ten

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