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Yanfei Kang1, Rob J Hyndman2, Kate Smith-Miles3

  1. School of Statistics, Renmin University of China.
  2. Department of Econometrics and Business Statistics, Monash University, Australia.
  3. School of Mathematical Sciences, Monash University, Australia.

International Journal of Forecasting (2017). 33(2), 345-358.

Abstract
It is common practice to evaluate the strength of forecasting methods using collections of well-studied time series datasets, such as the M3 data. But how diverse are these time series, how challenging, and do they enable us to study the unique strengths and weaknesses of different forecasting methods? In this paper we propose a visualisation method for a collection of time series that enables a time series to be represented as a point in a 2-dimensional instance space. The effectiveness of different forecasting methods can be visualised easily across this space, and the diversity of the time series in an existing collection can be assessed. Noting that the M3 dataset is not as diverse as we would ideally like, this paper also proposes a method for generating new time series with controllable characteristics to fill in and spread out the instance space, making generalisations of forecasting method performance as robust as possible.

Download working paper

Online paper

  Tag: exponential smoothing

3 posts
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, […]

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 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 […]