  This paper introduces a novel meta-learning algorithm for time series
forecast model performance prediction. We model the forecast error as a
function of time series features calculated from the historical time series
with an efficient Bayesian multivariate surface regression approach. The
minimum predicted forecast error is then used to identify an individual model
or a combination of models to produce the final forecasts. It is well-known
that the performance of most meta-learning models depends on the
representativeness of the reference dataset used for training. In such
circumstances, we augment the reference dataset with a feature-based time
series simulation approach, namely GRATIS, in generating a rich and
representative time series collection. The proposed framework is tested using
the M4 competition data and is compared against commonly used forecasting
approaches. Our approach provides comparable performances to other model
selection/combination approaches but at a lower computational cost and a higher
degree of interpretability, which is important for supporting decisions. We
also provide useful insights regarding which forecasting models are expected to
work better for particular types of time series, the intrinsic mechanisms of
the meta-learners and how the forecasting performances are affected by various
factors.
