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

Ioannis Nasios and Konstantinos Vogklis

Papers from arXiv.org

Abstract: 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

Date: 2023-10
New Economics Papers: this item is included in nep-big, nep-cmp and nep-for
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Published in Volume 38, Issue 4, 2022, Pages 1448-1459

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2310.13029

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