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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: graphics

105 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 […]

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 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.

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.

September 23rd, 2014

Forecasting: principles and practice (UWA course)

Workshop held at UWA on 23-25 September 2014.

October 19th, 2013

hdrcde package for R

The hdrcde package provides tools for computation of highest density regions in one and two dimensions, kernel estimation of univariate […]

August 28th, 2013

rainbow package for R

The rainbow package provides tools for plotting functional data including the rainbow plot, functional bagplot, functional HDR boxplot. The methods […]

August 3rd, 2010

Exploratory graphics for functional data

Han Lin Shang and Rob J Hyndman Department of Econometrics and Business Statistics, Monash University, Clayton, Australia Interface 2010: Computing Science […]

March 1st, 2010

Rainbow plots, bagplots and boxplots for functional data

Rob J Hyndman and Han Lin Shang Journal of Computational and Graphical Statistics (2010), 19(1), 29-45. Abstract: We propose new […]

June 19th, 2008

Bagplots, boxplots and outlier detection for functional data

Australian Statistics Conference. Melbourne, July 2008. When: June 19-21, 2008 Where: First International Workshop on Functional and Operatorial Statistics, Toulouse […]

May 15th, 2008

Bagplots, boxplots and outlier detection for functional data

Rob J Hyndman and Han Lin Shang (2008) In Dabo-Niang, S., and Ferraty, F. (eds), Functional and Operatorial Statistics, chap […]

July 16th, 2005

Book Review of Maindonald and Braun. “Data Analysis and Graphics Using R: An Example-based Approach”, Cambridge University Press, 2003.

November 16th, 2000

Residual diagnostic plots for model mis-specification in time series regression

Australian and New Zealand Journal of Statistics (2000), 42(4), 463-477. Richard Fraccaro1,2, Rob J Hyndman1 and Alan Veevers2 Department of […]

July 16th, 1996

Estimating and visualizing conditional densities

Journal of Computational and Graphical Statistics (1996), 5 315-336. Rob J Hyndman1 and David Bashtannyk1 Abstract: We consider the kernel […]

July 16th, 1996

Computing and graphing highest density regions

American Statistician (1996),50, 120-126. Rob J Hyndman1 Abstract: Many statistical methods involve summarizing a probability distribution by a region of […]