Visualising forecasting Algorithm Performance using Time Series Instance Spaces

Yanfei Kang (), Rob Hyndman and Kate Smith-Miles ()

No 10/16, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

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.

Keywords: M3-competition; time series visualisation; time series generation; forecasting algorithm comparison (search for similar items in EconPapers)
JEL-codes: C52 C53 C55 (search for similar items in EconPapers)
Pages: 23
Date: 2016
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-net
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Journal Article: Visualising forecasting algorithm performance using time series instance spaces (2017)
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