A0700
Title: Forecasting performance evaluation by time series visualisation and generation
Authors: Yanfei Kang - Beihang University (China) [presenting]
Rob Hyndman - Monash University (Australia)
Kate Smith-Miles - Monash University (Australia)
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 not so common to determine how diverse these time series are, how challenging, and whether they enable us to study the unique strengths and weaknesses of different forecasting methods. 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 diversity of the M3 dataset has been questioned, a method is also proposed 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.
