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 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier B.V
01.04.2017
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| Témata: | |
| ISSN: | 0169-2070, 1872-8200 |
| On-line přístup: | Získat plný text |
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| 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. |
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| AbstractList | 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. |
| Author | Kang, Yanfei Hyndman, Rob J. Smith-Miles, Kate |
| Author_xml | – sequence: 1 givenname: Yanfei surname: Kang fullname: Kang, Yanfei email: yanfei.kang@outlook.com organization: School of Economics and Management, Beihang University, Beijing, 100191, China – sequence: 2 givenname: Rob J. surname: Hyndman fullname: Hyndman, Rob J. email: Rob.Hyndman@monash.edu organization: Department of Econometrics and Business Statistics, Monash University, Clayton VIC 3800, Australia – sequence: 3 givenname: Kate surname: Smith-Miles fullname: Smith-Miles, Kate email: Kate.Smith-Miles@monash.edu organization: School of Mathematical Sciences, Monash University, Clayton VIC 3800, Australia |
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