George Athanasopoulosa, Rob J. Hyndmana, Nikolaos Kourentzesb, Fotios Petropoulosc

a Department of Econometrics and Business Statistics, Monash University, Australia
b Lancaster University Management School, Department of Management Science, UK
c School of Management, University of Bath, UK

European Journal of Operational Research (2017), to appear.

This paper introduces the concept of Temporal Hierarchies for time series forecasting. A temporal hierarchy can be constructed for any time series by means of non-overlapping temporal aggregation. Predictions constructed at all aggregation levels are combined with the proposed framework to result in temporally reconciled, accurate and robust forecasts. The implied combination mitigates modelling uncertainty, while the reconciled nature of the forecasts results in a unified prediction that supports aligned decisions at different planning horizons: from short-term operational up to long-term strategic planning. The proposed methodology is independent of forecasting models. It can embed high level managerial forecasts that incorporate complex and unstructured information with lower level statistical forecasts. Our results show that forecasting with temporal hierarchies increases accuracy over conventional forecasting, particularly under increased modelling uncertainty. We discuss organisational implications of the temporally reconciled forecasts using a case study of Accident & Emergency departments.

Keywords: Hierarchical forecasting, temporal aggregation, reconciliation, forecast combination

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  Tag: time series

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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, […]

October 13th, 2016

Reconciling forecasts: the hts and thief packages

Talk given at eRum2016, Poznań, Poland.  

September 15th, 2016

Forecasting large collections of related time series

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

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.  

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 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 […]

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.

August 17th, 2015

Machine learning bootcamp

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

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 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.

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.

January 12th, 2015

Visualizing and forecasting big time series data

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

June 24th, 2014

Functional time series with applications in demography

A short course given at Humboldt University, Berlin, 24-25 June 2014.

February 13th, 2014

Automatic time series forecasting

Talk presented at the conference “New Trends on Intelligent Systems and Soft Computing 2014”, University of Granada, Spain. 13-14 February […]

February 1st, 2014

demography: Forecasting mortality, fertility, migration and population data

The demography package for R contains functions for various demographic analyses. It provides facilities for demographic statistics, modelling and forecasting. […]

January 10th, 2014

Boosting multi-step autoregressive forecasts

Souhaib Ben Taieb and Rob J Hyndman International Conference on Machine Learning (ICML) 2014. Abstract Multi-step forecasts can be produced […]

January 1st, 2014

Forecasting: principles and practice


October 11th, 2013

Coherent mortality forecasting using functional time series

A talk given today at Macquarie University, Sydney.

August 29th, 2013

ftsa package for R

The ftsa package provides tools for modelling and forecasting functional time series.

August 22nd, 2013

fds package for R

The fds package provides functional data sets useful for testing new methods.

June 25th, 2013

Forecasting without forecasters

A keynote talk given at the International Symposium on Forecasting, Seoul, South Korea. 25 June 2013.

June 19th, 2013

Mcomp package for R

The Mcomp package for R provides the 1001 time series from the M-competition (Makridakis et al. 1982) and the 3003 […]

March 14th, 2013

fpp package for R

The fpp package for R provides all data sets required for the examples and exercises in the book Forecasting: principles and […]

February 1st, 2013

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 […]

October 30th, 2012

expsmooth package for R

The expsmooth package for R provides data sets from the book “Forecasting with exponential smoothing: the state space approach” by […]

October 30th, 2012

fma package for R

The fma package for R provides all data sets from “Forecasting: methods and applications” by Makridakis, Wheelwright & Hyndman (Wiley, […]

September 2nd, 2012

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, […]

June 19th, 2012

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 […]

January 30th, 2012

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 […]

December 31st, 2011

Forecasting time series with complex seasonal patterns using exponential smoothing

Alysha M De Livera, Rob J Hyndman and Ralph D Snyder Journal of the American Statistical Association (2011) 106(496), 1513-1527. […]

October 27th, 2011

Forecasting time series using R

Melbourne R Users’ Group Thursday, October 27, 2011, 6:00 PM Deloitte, Level 11 (Culture Room), 550 Bourke Street, Melbourne I […]

July 15th, 2011

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 […]

February 9th, 2011

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), […]

November 16th, 2010

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 […]

September 7th, 2010

Demographic forecasting using functional data analysis

University of Wollongong, 8 September 2010. Statistical Society of Australia, Victorian Branch, 28 September 2010. Updated version. September 2012. Abstract: […]

August 17th, 2010

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 […]

June 9th, 2010

Coherent functional forecasts of mortality rates and life expectancy

Talk to be given at the International Symposium on Forecasting, San Diego, 20-23 June 2010. Slides

May 6th, 2010

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 […]

February 6th, 2010

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 […]

January 15th, 2010

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 […]