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

arXiv: https://arxiv.org/abs/2310.13029 · HTML: https://mlsanity.com/paper/2310.13029/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 19 Oct 2023.

## Builds on (most cited first, 4 of 4)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [fastai: A Layered API for Deep Learning](https://mlsanity.com/paper/2002.04688/) (2020)
- [Polyglot: Distributed Word Representations for Multilingual NLP](https://mlsanity.com/paper/1307.1662/) (2013)
- [Tweedie Gradient Boosting for Extremely Unbalanced Zero-inflated Data](https://mlsanity.com/paper/1811.10192/) (2018)

## Built on by (most cited first, 5 of 5)

- [Enhancing kelp forest detection in remote sensing images using crowdsourced labels with Mixed Vision Transformers and ConvNeXt segmentation models](https://mlsanity.com/paper/2501.14001/) (2025)
- [Forecasting with Hyper-Trees](https://mlsanity.com/paper/2405.07836/) (2024)
- [The ecosystem of machine learning competitions: Platforms, participants, and their impact on AI development](https://mlsanity.com/paper/2604.08001/) (2026)
- [Hierarchical Spatio-Temporal Transformer for Coherent Emergency Department Forecasting](https://mlsanity.com/paper/2607.27106/) (2026)
- [PaGNet: A Panel-Aware GBDT--Neural Network for Multi-Target Corporate Tax Avoidance Proxy Forecasting](https://mlsanity.com/paper/2609.20177/) (2026)

## Abstract (arXiv)

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