  This paper studies the application of ensembles composed of multi-output
models for multi-step ahead forecasting problems. Dynamic ensembles have been
commonly used for forecasting. However, these are typically designed for
one-step-ahead tasks. On the other hand, the literature regarding the
application of dynamic ensembles for multi-step ahead forecasting is scarce.
Moreover, it is not clear how the combination rule is applied across the
forecasting horizon. We carried out extensive experiments to analyze the
application of dynamic ensembles for multi-step forecasting. We resorted to a
case study with 3568 time series and an ensemble of 30 multi-output models. We
discovered that dynamic ensembles based on arbitrating and windowing present
the best performance according to average rank. Moreover, as the horizon
increases, most approaches struggle to outperform a static ensemble that
assigns equal weights to all models. The experiments are publicly available in
a repository.
