Visualising forecasting algorithm performance using time series instance spaces

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 differe...

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Vydáno v:	International journal of forecasting Ročník 33; číslo 2; s. 345 - 358
Hlavní autoři:	Kang, Yanfei, Hyndman, Rob J., Smith-Miles, Kate
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 01.04.2017
Témata:	Forecasting algorithm comparison M3-Competition Time series generation Time series visualisation M3-Competition Forecasting algorithm comparison Time series visualisation Time series generation
ISSN:	0169-2070, 1872-8200
On-line přístup:	Získat plný text
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Popis
Shrnutí:	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.
ISSN:	0169-2070 1872-8200
DOI:	10.1016/j.ijforecast.2016.09.004