Abstract

There are no algorithms that generally perform better or worse than random when looking at all possible data sets according to the no-free-lunch theorem. A specific forecasting method will hence naturally have different performances in different empirical studies. This makes it impossible to draw general conclusions, however, there will of course be specific problems for which one algorithm performs better than another in practice. Meta-learning exploits this fact by linking characteristics of the data set to the performances of methods, adapting the selection or combination of base methods to a specific problem. This contribution describes an approach using meta-learning for time series forecasting in the NN GC1 competition. A pool of individual forecasting and forecast combination models are combined using a ranking algorithm with weights being determined by past performance on similar series.