Machine learning applications in hierarchical time series forecasting: investigating the impact of promotions

Hierarchical forecasting is needed in many situations in the supply chain to support decision making. Top-down, bottom-up, and optimal linear combination methods are common in hierarchical forecasting. There is no universally optimal solution for hierarchical forecasting, and each method has some advantages and disadvantages. While top-down and bottom-up methods use only the information at the top and bottom levels, respectively, linear combinations use the individual sales forecasts from all series and levels and combine them linearly, often outperforming the conventional top-down and bottom-up methods. These methods do not directly utilise the explanatory information such as price and promotion status that may be available across different levels in the hierarchy, and their performance may be impacted by these external factors. We propose to use a multi-output regression model that utilises the explanatory variables from across hierarchical levels to simultaneously generate forecasts for all the series at the bottom level. We perform an in-depth analysis of 55 sets of fast-moving consumer goods time series and 3049 products of the M5 forecasting competition data. Our results show that our proposed algorithm effectively utilises explanatory variables from across the hierarchy to generate reliable forecasts for different hierarchical levels, especially in the presence of deep promotional discounts.