Hierarchical forecasting with a top-down alignment of independent level forecasts

Hierarchical forecasting with intermittent time series is a challenge in both research and empirical studies. Vast research focuses on improving the accuracy of each hierarchy, especially the intermittent time series at bottom levels. It then reconciles forecasts at each hierarchy to further improve the overall performance. In this paper, we present a hierarchical forecasting 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 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 a simple yet effective variant of the bottom-up method, which accounts 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 approach is also business orientated and could be beneficial for business strategic planning.