Rob J Hyndman

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George AthanasopoulosMonash UniversityVerified email at monash.edu
Ralph SnyderAdjuct Associate Professor, Monash UniversityVerified email at monash.edu
Kate Smith-MilesProfessor of Applied Mathematics, The University of MelbourneVerified email at unimelb.edu.au
Han Lin ShangDepartment of Actuarial Studies and Business Analytics, Macquarie UniversityVerified email at mq.edu.au
Keith OrdEmeritus Professor of Business Statistics, Georgetown UniversityVerified email at georgetown.edu
Spyros MakridakisUniversity of Nicosia, Inst. For the Future (IFF), the Makridakis Open Forecast. Center MOFCVerified email at UNIC.ac.cy
Christoph BergmeirUniversity of GranadaVerified email at monash.edu
Heather BoothAustralian National UniversityVerified email at anu.edu.au
Mitchell O'Hara-WildResearch Assistant, Monash UniversityVerified email at mitchelloharawild.com
Jan VerbesseltAssociate Professor, Wageningen UniversityVerified email at wur.nl
Bircan ErbasLa Trobe UniversityVerified email at latrobe.edu.au
Souhaib Ben TaiebAssociate Professor in Machine Learning, University of Mons, BelgiumVerified email at umons.ac.be
Fotios PetropoulosProfessor of Management Science, University of BathVerified email at bath.ac.uk
Yeasmin KhandakarSenior Data Scientist, TransurbanVerified email at officeworks.com.au
Darius CulvenorEnvironmental Sensing SystemsVerified email at sensingsystems.com.au
Dr. Farah YasmeenAssistant Professor Statistics Karachi UniversityVerified email at uok.edu.pk
Shanika WickramasuriyaLecturer, University of AucklandVerified email at auckland.ac.nz
Anastasios PanagiotelisAssociate Professor of Business Analytics, University of SydneyVerified email at sydney.edu.au
Leonie TickleDeputy Dean Research and Innovation, Macquarie Business SchoolVerified email at mq.edu.au
Sevvandi KandanaarachchiCSIRO's Data61Verified email at data61.csiro.au

Rob J Hyndman

Professor of Statistics, Monash University

Verified email at monash.edu - Homepage

Forecasting Time series Statistics Exploratory data analysis Anomaly detection


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Forecasting methods and applications S Makridakis, SC Wheelwright, RJ Hyndman John Wiley & Sons, 1998	7098*	1998
Forecasting: principles and practice RJ Hyndman, G Athanasopoulos OTexts, 2018	6329	2018
Another look at measures of forecast accuracy RJ Hyndman, AB Koehler International journal of forecasting 22 (4), 679-688, 2006	5397	2006
Automatic time series forecasting: the forecast package for R RJ Hyndman, Y Khandakar Journal of Statistical Software, 2007	4070	2007
Forecasting with exponential smoothing: the state space approach RJ Hyndman, AB Koehler, JK Ord, RD Snyder Springer Verlag, 2008	1762	2008
Detecting trend and seasonal changes in satellite image time series J Verbesselt, R Hyndman, G Newnham, D Culvenor Remote sensing of Environment 114 (1), 106-115, 2010	1664	2010
forecast: Forecasting functions for time series and linear models RJ Hyndman	1544*	2021
25 years of time series forecasting JG De Gooijer, RJ Hyndman International journal of forecasting 22 (3), 443-473, 2006	1437	2006
Sample quantiles in statistical packages RJ Hyndman, Y Fan The American Statistician 50 (4), 361-365, 1996	1273	1996
A state space framework for automatic forecasting using exponential smoothing methods RJ Hyndman, AB Koehler, RD Snyder, S Grose International Journal of forecasting 18 (3), 439-454, 2002	1229	2002
Forecasting time series with complex seasonal patterns using exponential smoothing AM De Livera, RJ Hyndman, RD Snyder Journal of the American statistical association 106 (496), 1513-1527, 2011	1018	2011
Robust forecasting of mortality and fertility rates: A functional data approach RJ Hyndman, MS Ullah Computational Statistics and Data Analysis 51 (10), 4942-4956, 2007	847	2007
Probabilistic energy forecasting: Global energy forecasting competition 2014 and beyond T Hong, P Pinson, S Fan, H Zareipour, A Troccoli, RJ Hyndman International Journal of forecasting 32 (3), 896-913, 2016	779	2016
Computing and graphing highest density regions RJ Hyndman The American Statistician 50 (2), 120-126, 1996	772	1996
Phenological change detection while accounting for abrupt and gradual trends in satellite image time series J Verbesselt, R Hyndman, A Zeileis, D Culvenor Remote Sensing of Environment 114 (12), 2970-2980, 2010	746	2010
Characteristic-based clustering for time series data X Wang, K Smith, R Hyndman Data mining and knowledge Discovery 13, 335-364, 2006	734	2006
Forecasting Methods for Managers S Makridakis, SC Wheelwright, RJ Hyndman Auflage. New York et al.: Wiley, 1989	667	1989
rmarkdown: Dynamic Documents for R J Allaire, Y Xie, J McPherson, J Luraschi, K Ushey, A Atkins, H Wickham, ... R package version 1 (11), 2018	630*	2018
Short-term load forecasting based on a semi-parametric additive model S Fan, RJ Hyndman IEEE transactions on power systems 27 (1), 134-141, 2011	550	2011
A note on the validity of cross-validation for evaluating autoregressive time series prediction C Bergmeir, RJ Hyndman, B Koo Computational Statistics & Data Analysis 120, 70-83, 2018	542	2018

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