Scalable Probabilistic Forecasting in Retail with Gradient Boosted Trees: A Practitioner's Approach. (arXiv:2311.00993v1 [cs.LG])

The recent M5 competition has advanced the state-of-the-art in retail
forecasting. However, we notice 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 assortment than brick-and-mortar
retailers, leading to more intermittent data. To scale to larger dataset sizes
with feasible computational effort, firstly, we investigate a two-layer
hierarchy and propose a top-down …

advanced art arxiv challenge challenges commerce competition datasets differences e-commerce face forecasting gradient gradient boosted trees retail scalable series state time series trees