  In numerous applications, it is required to produce forecasts for multiple
time-series at different hierarchy levels. An obvious example is given by the
supply chain in which demand forecasting may be needed at a store, city, or
country level. The independent forecasts typically do not add up properly
because of the hierarchical constraints, so a reconciliation step is needed. In
this paper, we propose a new general, flexible, and easy-to-implement
reconciliation strategy based on an encoder-decoder neural network. By testing
our method on four real-world datasets, we show that it can consistently reach
or surpass the performance of existing methods in the reconciliation setting.
