  Hierarchical forecasting with intermittent time series is a challenge in both
research and empirical studies. The overall forecasting performance is heavily
affected by the forecasting accuracy of intermittent time series at bottom
levels. In this paper, we present a forecasting reconciliation approach that
treats the bottom level forecast as latent to ensure higher forecasting
accuracy on the upper levels of the hierarchy. We employ a pure deep learning
forecasting approach N-BEATS for continuous time series on top levels and a
widely used tree-based algorithm LightGBM for the bottom level intermittent
time series. The hierarchical forecasting with alignment approach is simple and
straightforward to implement in practice. It sheds light on an orthogonal
direction for forecasting reconciliation. When there is difficulty finding an
optimal reconciliation, allowing suboptimal forecasts at a lower level could
retain a high overall performance. The approach in this empirical study was
developed by the first author during the M5 Forecasting Accuracy competition
ranking second place. The approach is business orientated and could be
beneficial for business strategic planning.
