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.
| Original language | English |
|---|---|
| Pages (from-to) | 345-358 |
| Number of pages | 14 |
| Journal | International Journal of Forecasting |
| Volume | 33 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 1 Apr 2017 |
Keywords
- Forecasting algorithm comparison
- M3-Competition
- Time series generation
- Time series visualisation
Cite this
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Visualising forecasting algorithm performance using time series instance spaces. / Kang, Yanfei; Hyndman, Rob; Smith-Miles, Kate.
In: International Journal of Forecasting, Vol. 33, No. 2, 01.04.2017, p. 345-358.Research output: Contribution to journal › Article › Research › peer-review
TY - JOUR
T1 - Visualising forecasting algorithm performance using time series instance spaces
AU - Kang, Yanfei
AU - Hyndman, Rob
AU - Smith-Miles, Kate
PY - 2017/4/1
Y1 - 2017/4/1
N2 - 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.
AB - 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.
KW - Forecasting algorithm comparison
KW - M3-Competition
KW - Time series generation
KW - Time series visualisation
UR - http://www.scopus.com/inward/record.url?scp=85009115385&partnerID=8YFLogxK
U2 - 10.1016/j.ijforecast.2016.09.004
DO - 10.1016/j.ijforecast.2016.09.004
M3 - Article
VL - 33
SP - 345
EP - 358
JO - International Journal of Forecasting
JF - International Journal of Forecasting
SN - 0169-2070
IS - 2
ER -