Blending gradient boosted trees and neural networks for point and probabilistic forecasting of hierarchical time series

In this study, we addressed the problem of point and probabilistic forecasting by describing a blending methodology for machine learning models from the gradient boosted trees and neural networks families. These principles were successfully applied in the recent M5 Competition in both the Accuracy and Uncertainty tracks. The key points of our methodology are: (a) transforming the task into regression on sales for a single day; (b) information-rich feature engineering; (c) creating a diverse set of state-of-the-art machine learning models; and (d) carefully constructing validation sets for model tuning. We show that the diversity of the machine learning models and careful selection of validation examples are most important for the effectiveness of our approach. Forecasting data have an inherent hierarchical structure (12 levels) but none of our proposed solutions exploited the hierarchical scheme. Using the proposed methodology, we ranked within the gold medal range in the Accuracy track and within the prizes in the Uncertainty track. Inference code with pre-trained models are available on GitHub.¹