Visualising forecasting algorithm performance using time series instance spaces

Yanfei Kang, Rob Hyndman, Kate Smith-Miles

Research output: Contribution to journalArticleResearchpeer-review

84 Citations (Scopus)


It is common practice to evaluate the strength of forecasting methods using collections of well-studied time series datasets, such as the M3 data. The question is, though, how diverse and challenging are these time series, and do they enable us to study the unique strengths and weaknesses of different forecasting methods? This paper proposes a visualisation method for collections of time series that enables a time series to be represented as a point in a two-dimensional instance space. The effectiveness of different forecasting methods across this space is easy to visualise, and the diversity of the time series in an existing collection can be assessed. Noting that the diversity of the M3 dataset has been questioned, this paper also proposes a method for generating new time series with controllable characteristics in order to fill in and spread out the instance space, making our generalisations of forecasting method performances as robust as possible.

Original languageEnglish
Pages (from-to)345-358
Number of pages14
JournalInternational Journal of Forecasting
Issue number2
Publication statusPublished - 1 Apr 2017


  • Forecasting algorithm comparison
  • M3-Competition
  • Time series generation
  • Time series visualisation

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