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

TL;DR

This paper introduces a hierarchical forecasting method that aligns bottom-level forecasts with top-level accuracy using deep learning and tree-based models, improving overall hierarchical forecast performance.

Contribution

It presents a novel hierarchical-forecasting-with-alignment approach that treats bottom-level forecasts as adjustable to enhance top-level accuracy, combining N-BEATS and LightGBM models.

Findings

Ranked second in the M5 Forecasting Accuracy competition

Effective in handling intermittent time series

Improves overall hierarchical forecast accuracy

Abstract

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.