Blending gradient boosted trees and neural networks for point and
  probabilistic forecasting of hierarchical time series
Ioannis Nasios Konstantinos Vogklis
In this paper we tackle the problem of point and probabilistic forecasting by
describing a blending methodology of machine learning models that belong to
gradient boosted trees and neural networks families. These principles were
successfully applied in the recent M5 Competition on both Accuracy and
Uncertainty tracks. The keypoints of our methodology are: a) transform the task
to regression on sales for a single day b) information rich feature engineering
c) create a diverse set of state-of-the-art machine learning models and d)
carefully construct validation sets for model tuning. We argue that the
diversity of the machine learning models along with the careful selection of
validation examples, where the most important ingredients for the effectiveness
of our approach. Although forecasting data had an inherent hierarchy structure
(12 levels), none of our proposed solutions exploited that hierarchical scheme.
Using the proposed methodology, our team was ranked within the gold medal range
in both Accuracy and the Uncertainty track. Inference code along with already
trained models are available at
https://github.com/IoannisNasios/M5_Uncertainty_3rd_place