Workshop to be held on 23-25 September 2014.

Venue: The University Club, University of Western Australia, Nedlands WA.

Requirements: a laptop with R installed, along with the fpp package and its dependencies. We will also use the hts and vars package on the third day.



  1. Introduction to forecasting [Slides, R code, Lab solutions]
  2. Forecasting tools [Slides, R code, Lab solutions]
  3. Exponential smoothing I [Slides, R code, Lab solutions]
  4. Exponential smoothing II [Slides, R code, Lab solutions]
  5. Time series decomposition and cross-validation [Slides, R code, Lab solutions]
  6. Transformations, stationarity and differencing [Slides, R code, Lab solutions]
  7. Non-seasonal ARIMA models [Slides, R code, Lab solutions]
  8. Seasonal ARIMA models [Slides, R code, Lab solutions]
  9. State space models [Slides, R code, Lab solutions]
  10. Dynamic regression [Slides, R code, Lab solutions]
  11. Hierarchical forecasting [Slides, R code, Lab solutions]
  12. Advanced methods [Slides, R code, Lab solutions]

Course Notes

Forecasting resources

  Tag: state space models

15 posts
September 23rd, 2014

Forecasting: principles and practice (UWA course)

Workshop held at UWA on 23-25 September 2014.

May 30th, 2014

State space models

A one-day workshop for the Australian Bureau of Statistics

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. […]

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 […]

November 26th, 2009

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 […]

January 1st, 2009

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 […]

November 16th, 2008

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 […]

August 17th, 2008

Forecasting with exponential smoothing: the state space approach

Hyndman, Koehler, Ord and Snyder (2008), Forecasting with exponential smoothing: the state space approach, Springer.

June 16th, 2008

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 […]

June 15th, 2008

Exponential smoothing and non-negative data

When: 22-25 June 2008 Where: International Symposium on Forecasting, Nice, France Abstract: The most common forecasting methods in business are […]

May 29th, 2007

A state space model for exponential smoothing with group seasonality

Pim Ouwehand1 , Rob J Hyndman2 , Ton G. de Kok1 and Karel H. van Donselaar1 Department of Technology Management, […]

April 2nd, 2005

Time series forecasting: the case for the single source of error state space approach

J. Keith Ord1 , Ralph D. Snyder2 , Anne B. Koehler3 , Rob J. Hyndman2 and Mark Leeds4 320 Old […]

January 16th, 2005

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 […]

July 16th, 2002

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 […]