  Hierarchical forecasting with intermittent time series is a challenge in both
research and empirical studies. Extensive research focuses on improving the
accuracy of each hierarchy, especially the intermittent time series at bottom
levels. Then hierarchical reconciliation could be used to improve the overall
performance further. In this paper, we present a
\emph{hierarchical-forecasting-with-alignment} approach that treats the bottom
level forecasts as mutable 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 at the top levels and a widely used
tree-based algorithm LightGBM for the intermittent time series at the bottom
level. The \emph{hierarchical-forecasting-with-alignment} approach is a simple
yet effective variant of the bottom-up method, accounting for biases that are
difficult to observe at the bottom level. It allows suboptimal forecasts at the
lower level to retain a higher overall performance. The approach in this
empirical study was developed by the first author during the M5 Forecasting
Accuracy competition, ranking second place. The method is also business
orientated and could benefit for business strategic planning.
