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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: accuracy

20 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, […]

January 25th, 2016

Probabilistic Energy Forecasting: Global Energy Forecasting Competition 2014 and Beyond

Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli and Rob J Hyndman International Journal of Forecasting (2016), 32(3), 896–913. […]

December 31st, 2015

Measuring forecast accuracy

This is a chapter for a new book on forecasting in business.

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 […]

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

September 23rd, 2014

Forecasting: principles and practice (UWA course)

Workshop held at UWA on 23-25 September 2014.

November 16th, 2006

Another look at measures of forecast accuracy

International Journal of Forecasting (2006). 22(4), 679-688. Rob J. Hyndman1 and Anne B. Koehler2 Department of Econometrics and Business Statistics, […]

September 16th, 2006

Another look at measures of forecast accuracy for intermittent demand

Foresight: the International Journal of Applied Forecasting (2006). 4, 43-46. Rob J. Hyndman Abstract: Some of the proposed measures of […]