Visualising forecasting algorithm performance using time series instance spaces
Yanfei Kang, Rob J Hyndman, Kate Smith-Miles
INTERNATIONAL JOURNAL OF FORECASTING | ELSEVIER SCIENCE BV | Published : 2017
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 da..View full abstract
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Awarded by Australian Research Council
This research was supported by the Australian Research Council under grants DP120103678 and FL140100012. The authors are grateful to the handling editor Professor Dick Van Dijk and the two reviewers who made useful comments that improved the clarity of the paper.