  The predictive advantage of combining several different predictive models is
widely accepted. Particularly in time series forecasting problems, this
combination is often dynamic to cope with potential non-stationary sources of
variation present in the data. Despite their superior predictive performance,
ensemble methods entail two main limitations: high computational costs and lack
of transparency. These issues often preclude the deployment of such approaches,
in favour of simpler yet more efficient and reliable ones. In this paper, we
leverage the idea of model compression to address this problem in time series
forecasting tasks. Model compression approaches have been mostly unexplored for
forecasting. Their application in time series is challenging due to the
evolving nature of the data. Further, while the literature focuses on neural
networks, we apply model compression to distinct types of methods. In an
extensive set of experiments, we show that compressing dynamic forecasting
ensembles into an individual model leads to a comparable predictive performance
and a drastic reduction in computational costs. Further, the compressed
individual model with best average rank is a rule-based regression model. Thus,
model compression also leads to benefits in terms of model interpretability.
The experiments carried in this paper are fully reproducible.

Machine Learning;Machine Learning