  Model selection has been proven an effective strategy for improving accuracy
in time series forecasting applications. However, when dealing with
hierarchical time series, apart from selecting the most appropriate forecasting
model, forecasters have also to select a suitable method for reconciling the
base forecasts produced for each series to make sure they are coherent.
Although some hierarchical forecasting methods like minimum trace are strongly
supported both theoretically and empirically for reconciling the base
forecasts, there are still circumstances under which they might not produce the
most accurate results, being outperformed by other methods. In this paper we
propose an approach for dynamically selecting the most appropriate hierarchical
forecasting method and succeeding better forecasting accuracy along with
coherence. The approach, to be called conditional hierarchical forecasting, is
based on Machine Learning classification methods and uses time series features
as leading indicators for performing the selection for each hierarchy examined
considering a variety of alternatives. Our results suggest that conditional
hierarchical forecasting leads to significantly more accurate forecasts than
standard approaches, especially at lower hierarchical levels.
