  The contribution of this work is twofold: (1) We introduce a collection of
ensemble methods for time series forecasting to combine predictions from base
models. We demonstrate insights on the power of ensemble learning for
forecasting, showing experiment results on about 16000 openly available
datasets, from M4, M5, M3 competitions, as well as FRED (Federal Reserve
Economic Data) datasets. Whereas experiments show that ensembles provide a
benefit on forecasting results, there is no clear winning ensemble strategy
(plus hyperparameter configuration). Thus, in addition, (2), we propose a
meta-learning step to choose, for each dataset, the most appropriate ensemble
method and their hyperparameter configuration to run based on dataset
meta-features.
