  Hierarchical forecasting methods have been widely used to support aligned
decision-making by providing coherent forecasts at different aggregation
levels. Traditional hierarchical forecasting approaches, such as the bottom-up
and top-down methods, focus on a particular aggregation level to anchor the
forecasts. During the past decades, these have been replaced by a variety of
linear combination approaches that exploit information from the complete
hierarchy to produce more accurate forecasts. However, the performance of these
combination methods depends on the particularities of the examined series and
their relationships. This paper proposes a novel hierarchical forecasting
approach based on machine learning that deals with these limitations in three
important ways. First, the proposed method allows for a non-linear combination
of the base forecasts, thus being more general than the linear approaches.
Second, it structurally combines the objectives of improved post-sample
empirical forecasting accuracy and coherence. Finally, due to its non-linear
nature, our approach selectively combines the base forecasts in a direct and
automated way without requiring that the complete information must be used for
producing reconciled forecasts for each series and level. The proposed method
is evaluated both in terms of accuracy and bias using two different data sets
coming from the tourism and retail industries. Our results suggest that the
proposed method gives superior point forecasts than existing approaches,
especially when the series comprising the hierarchy are not characterized by
the same patterns.
