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March 28th, 2017

A note on upper bounds for forecast-value-added relative to naïve forecasts

Paul Goodwin, Fotios Petropoulos, Rob J Hyndman Journal of the Operational Research Society (2017), to appear. Abstract: In forecast value […]

March 7th, 2017

Coherent Probabilistic Forecasts for Hierarchical Time Series

Souhaib Ben Taieb, James W. Taylor, Rob J. Hyndman Abstract: Many applications require forecasts for a hierarchy comprising a set […]

February 28th, 2017

Forecasting with temporal hierarchies

George Athanasopoulosa, Rob J. Hyndmana, Nikolaos Kourentzesb, Fotios Petropoulosc a Department of Econometrics and Business Statistics, Monash University, Australia b […]

February 14th, 2017

The Australian Macro Database: An online resource for macroeconomic research in Australia

Timur Behlul, Anastasios Panagiotelis, George Athanasopoulos, Rob J Hyndman, Farshid Vahid Abstract A website that encourages and facilities the use […]

February 14th, 2017

Macroeconomic forecasting for Australia using a large number of predictors

Bin Jiang, George Athanasopoulos, Rob J Hyndman, Anastasios Panagiotelis, Farshid Vahid Abstract A popular approach to forecasting macroeconomic variables is […]

January 13th, 2017

Visualising forecasting algorithm performance using time series instance spaces

Yanfei Kang1, Rob J Hyndman2, Kate Smith-Miles3 School of Statistics, Renmin University of China. Department of Econometrics and Business Statistics, […]

January 1st, 2017

Associations between outdoor fungal spores and childhood and adolescent asthma hospitalisations

Rachel Tham, Don Vicendese, Shyamali C Dharmage, Rob J Hyndman, Ed Newbigin, Emma Lewis, Molly O’Sullivan, Adrian J Lowe, Philip […]

January 1st, 2017

Grouped functional time series forecasting: an application to age-specific mortality rates

Han Lin Shang and Rob J Hyndman Journal of Computational and Graphical Statistics (2017) to appear. Abstract Age-specific mortality rates […]

December 7th, 2016

Exploring the influence of short-term temperature patterns on temperature-related mortality: a case-study of Melbourne, Australia

John L. Pearce, Madison Hyer, Rob J. Hyndman, Margaret Loughnan, Martine Dennekamp and Neville Nicholls Environmental Health (2016), 15:107 Abstract: […]

October 13th, 2016

Reconciling forecasts: the hts and thief packages

Talk given at eRum2016, Poznań, Poland.   Related

September 20th, 2016

smoothAPC package for R

The smoothAPC package implements smoothing of demographic data. The method uses bivariate thin plate splines, bivariate lasso-type regularization, and allows […]

September 20th, 2016

stR package for R

The stR package implements STR time series decomposition. The methods are described in Dokumentov, A., and Hyndman, R.J. (2016) STR: […]

September 15th, 2016

Forecasting large collections of related time series

Talk given at the German Statistical Week, Augsburg, 15 September 2016 Related

August 22nd, 2016

thief package for R

The thief package provides tools for Temporal Hierarchical Forecasting. The methods are described in Forecasting with temporal hierarchies, co-authored with […]

June 21st, 2016

Exploring time series collections used for forecast evaluation

Talk given at the International Symposium on Forecasting. Monday 20 June 2016 Santander, Spain It is common practice to evaluate […]

May 25th, 2016

ISCRR time series workshop

Materials for one-day workshop on time series and forecasting presented to ISCRR, Melbourne. Textbook Hyndman and Athanasopoulos (2014): Software […]

May 6th, 2016

Automatic foRecasting using R

Talk given at the Melbourne Data Science Initiative, 6 May 2016.   Related

February 29th, 2016

On sampling methods for costly multi-objective black-box optimization

Ingrida Steponavičė, Mojdeh Shirazi-Manesh, Rob J. Hyndman, Kate Smith-Miles and Laura Villanova In Advances in Stochastic and Deterministic Global Optimization, […]

February 19th, 2016

Dynamic Algorithm Selection for Pareto Optimal Set Approximation

Ingrida Steponavičė, Rob J Hyndman, Kate Smith-Miles, Laura Villanova Journal of Global Optimization (2016), pp.1-20. Abstract: This paper presents a meta-algorithm […]

February 4th, 2016

Forecasting uncertainty in electricity smart meter data by boosting additive quantile regression

Souhaib BenTaieb, Raphael Huser, Rob J. Hyndman and Marc G. Genton IEEE Transactions on Smart Grid (2016), 7(5), 2448-2455. Abstract: […]

January 30th, 2016

Bayesian rank selection in multivariate regressions

Bin Jiang, Anastasios Panagiotelis, George Athanasopoulos, Rob J Hyndman, and Farshid Vahid. Department of Econometrics & Business Statistics, Monash University, […]

January 25th, 2016

Probabilistic Energy Forecasting: Global Energy Forecasting Competition 2014 and Beyond

Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli and Rob J Hyndman International Journal of Forecasting (2016), 32(3), 896–913. […]

January 24th, 2016

Long-term forecasts of age-specific participation rates with functional data models

Thomas Url1, Rob J Hyndman2, Alexander Dokumentov2 Vienna University of Economics and Business, Vienna, Austria Monash Business School, Monash University, […]

January 1st, 2016

Bagging exponential smoothing methods using STL decomposition and Box-Cox transformation

Christoph Bergmeir1, Rob J Hyndman2, José M Benítez1 Department of Computer Science and Artificial Intelligence, University of Granada, Spain. Department […]

January 1st, 2016

Fast computation of reconciled forecasts for hierarchical and grouped time series

By Rob J Hyndman, Ala Lee & Earo Wang

December 31st, 2015

Measuring forecast accuracy

This is a chapter for a new book on forecasting in business.

November 26th, 2015

Forecasting hierarchical and grouped time series through trace minimization

Shanika L Wickramasuriya, George Athanasopoulos, Rob J Hyndman Department of Econometrics and Business Statistics, Monash University   Abstract Large collections […]

November 2nd, 2015

Forecasting big time series data using R

Keynote address given at the Chinese R conference held in Nanchang, Jianxi province. 24-25 October 2015. Related

October 7th, 2015

Optimal forecast reconciliation for big time series data

Seminar given at Stanford University on 6th October, and University of California (Davis) on 8th October. Related

October 5th, 2015

Google workshop: Forecasting and visualizing big time series data

Workshop for Google, Mountain View, California. Monday 5 October 2015 Automatic algorithms for time series forecasting Optimal forecast reconciliation for […]

September 16th, 2015


A journey from faith via evidence. I was a Christian for nearly 30 years, and was well-known as a writer […]

August 25th, 2015

New IJF editors

Rob J Hyndman (2015) Editorial, International Journal of Forecasting. Online article. Related

August 17th, 2015

Machine learning bootcamp

A talk on time series forecasting for the Monash University Machine Learning Bootcamp. Demo R code Related

August 7th, 2015

Statistical issues with using herbarium data for the estimation of invasion lag-phases

By Rob J Hyndman, Mohsen B Mesgaran and Roger D Cousens

June 30th, 2015

Exploring the feature space of large collections of time series

Work­shop on Fron­tiers in Func­tional Data Analy­sis Banff, Canada.   It is becoming increasingly common for organizations to collect very […]

June 26th, 2015

Exploring the boundaries of predictability: what can we forecast, and when should we give up?

Yahoo Big Thinkers Sunnyvale, California Friday 26 June 2015, 3:00-4:00 pm Location: Yahoo Sunnyvale Campus and LIVE at   […]

June 25th, 2015

Automatic algorithms for time series forecasting

Google Mountain View, California.   Many applications require a large number of time series to be forecast completely automatically. For […]

June 23rd, 2015

MEFM: An R package for long-term probabilistic forecasting of electricity demand

International Symposium on Forecasting Riverside, California   I will describe and demonstrate a new open-source R package that implements the […]

June 19th, 2015

Probabilistic forecasting of peak electricity demand

Southern California Edison Rosemead, California   Electricity demand forecasting plays an important role in short-term load allocation and long-term planning […]

June 10th, 2015

Do human rhinovirus infections and food allergy modify grass pollen–induced asthma hospital admissions in children?

Erbas et al.
Journal of Allergy and Clinical Immunology (2015)

June 8th, 2015

STR: A Seasonal-Trend Decomposition Procedure Based on Regression

By Alex Dokumentov and Rob J Hyndman

June 4th, 2015

Probabilistic time series forecasting with boosted additive models: an application to smart meter data

By Souhaib Ben Taieb, Raphael Huser, Rob J Hyndman and Marc G Genton

June 1st, 2015

Large-scale unusual time series detection

Rob J Hyndman, Earo Wang and Nikolay Laptev

May 26th, 2015

Visualization of big time series data

Talk given to a joint meeting of the Statistical Society of Australia (Victorian branch) and the Melbourne Data Science Meetup Group.

May 22nd, 2015

Probabilistic forecasting of long-term peak electricity demand

The latest version of my talk on electricity demand forecasting.
Given to the “Monash Energy Materials and Systems Institute”

April 20th, 2015

A note on the validity of cross-validation for evaluating time series prediction

By Christoph Bergmeir, Rob J Hyndman, and Bonsoo Koo

April 4th, 2015

Discussion of “High-dimensional autocovariance matrices and optimal linear prediction”

My discussion of the article on “High-dimensional autocovariance matrices and optimal linear prediction”
by Timothy L. McMurry and Dimitris N. Politis.

Electronic J Statistics (2015) 9, 792-796.

April 1st, 2015

Change to the IJF editors

Rob J Hyndman (2015) Editorial, International Journal of Forecasting Online article Related

February 23rd, 2015

Visualization and forecasting of big time series data

Talk given at the ACEMS Big data workshop, QUT.

January 12th, 2015

Visualizing and forecasting big time series data

Talk given at the Institute of Statistical Science, Academia Sinica, Taipei, Taiwan.