@misc{10481/97464,
year = {2024},
month = {11},
url = {https://hdl.handle.net/10481/97464},
abstract = {The recent M5 competition has advanced the state-of-the-art in retail forecasting. However, there are important
differences between the competition challenge and the challenges we face in a large e-commerce company. The
datasets in our scenario are larger (hundreds of thousands of time series), and e-commerce can afford to have
a larger stock assortment than brick-and-mortar retailers, leading to more intermittent data. To scale to larger
dataset sizes with feasible computational effort, we investigate a two-layer hierarchy, namely the decision
level with product unit sales and an aggregated level, e.g., through warehouse-product aggregation, reducing
the number of series and degree of intermittency. We propose a top-down approach to forecasting at the
aggregated level, and then disaggregate to obtain decision-level forecasts. Probabilistic forecasts are generated
under distributional assumptions. The proposed scalable method is evaluated on both a large proprietary
dataset, as well as the publicly available Corporación Favorita and M5 datasets. We are able to show the
differences in characteristics of the e-commerce and brick-and-mortar retail datasets. Notably, our top-down
forecasting framework enters the top 50 of the original M5 competition, even with models trained at a higher
level under a much simpler setting.},
organization = {María Zambrano (Senior)Fellowship by the Spanish Ministry of Universities},
organization = {Next Generation funds from the European Union},
publisher = {Elsevier},
keywords = {Probabilistic forecasting},
keywords = {Gradient boosted trees},
keywords = {Global models},
title = {Scalable probabilistic forecasting in retail with gradient boosted trees: A practitioner’s approach},
doi = {10.1016/j.ijpe.2024.109449},
author = {Long, Xueying and Bui, Quang and Oktavian, Grady and F. Schmidt, Daniel and Bergmeir, Christoph Norbert and Godahewa, Rakshitha and Per Lee, Seong and Zhao, Kaifeng and Condylis, Paul},
}