Visualising forecasting algorithm performance using time series instance spaces

Yanfei Kang, Rob Hyndman and Kate Smith-Miles

International Journal of Forecasting, 2017, vol. 33, issue 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. 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.

Keywords: M3-Competition; Time series visualisation; Time series generation; Forecasting algorithm comparison (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (33)

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Working Paper: Visualising forecasting Algorithm Performance using Time Series Instance Spaces (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:33:y:2017:i:2:p:345-358

DOI: 10.1016/j.ijforecast.2016.09.004

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