A white-boxed ISSM approach to estimate uncertainty distributions of Walmart sales

Rafael de Rezende, Katharina Egert, Ignacio Marin and Guilherme Thompson

International Journal of Forecasting, 2022, vol. 38, issue 4, 1460-1467

Abstract: We present our solution for the M5 Uncertainty competition. Our solution ranked sixth out of 909 submissions across all hierarchical levels and ranked first for prediction at the finest level of granularity (product-store sales, i.e. SKUs). The model combines a multi-stage state-space model and Monte Carlo simulations to generate the forecasting scenarios (trajectories). Observed sales are modelled with negative binomial distributions to represent discrete over-dispersed sales. Seasonal factors are handcrafted and modelled with linear coefficients that are calculated at the store-department level.

Keywords: Sales forecasting; Probabilistic forecasting; Time series; Count data; M-competitions; State-space models; Exponential smoothing; Negative binomial (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:38:y:2022:i:4:p:1460-1467

DOI: 10.1016/j.ijforecast.2021.11.006

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