Projects per year
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 |
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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
Projects
- 2 Finished
-
Stress-testing algorithms: generating new test instances to elicit insights
Smith-Miles, K.
Australian Research Council (ARC), Monash University
8/12/14 → 31/12/19
Project: Research
-
Footprints in instance space: visualising the suitability of optimisation algorithms
Smith-Miles, K. & Burke, E.
Australian Research Council (ARC), Monash University
3/01/12 → 13/09/17
Project: Research