List

Materials for one-day workshop on time series and forecasting presented to ISCRR, Melbourne.

Textbook

Hyndman and Athanasopoulos (2014): OTexts.org/fpp

Software

Make sure you have a recent version of R and RStudio, and have installed the fpp and ggplot2 packages and all their dependencies.

Outline

  1. Introduction: Slides
  2. Time series visualization Slides [R code] [GDP data]
  3. Benchmark forecasting and time series decomposition Slides [R code]
  4. Exponential smoothing methods Slides [R code]
  5. ARIMA models Slides [R code]

Lab sessions

  Tag: exponential smoothing

3 posts
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): OTexts.org/fpp Software […]

January 13th, 2016

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, 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 […]

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

May 8th, 2014

forecast package for R

The forecast package for R provides methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling.

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 […]

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 […]

July 6th, 2009

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. […]

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.

July 16th, 2008

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 […]

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 […]

February 1st, 2008

Modelling and forecasting Australian domestic tourism

Tourism Management (2008), 29(1), 19-31. George Athanasopoulos and Rob J. Hyndman Abstract: In this paper, we model and forecast Australian […]

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 […]

January 16th, 2005

Local linear forecasts using cubic smoothing splines

Australian and New Zealand Journal of Statistics (2005), 47(1), 87-99. Rob J. Hyndman1, Maxwell L. King1, Ivet Pitrun1 and Baki […]

May 16th, 2004

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 […]

April 16th, 2003

Unmasking the Theta method

International Journal of Forecasting (2003), 19, 287-290. Rob J. Hyndman and Baki Billah Abstract: The “Theta method” of forecasting performed […]

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 […]