World Models, Trees and Foundation Models for Retail Forecasting: Evidence from Four Public Data Sets

Key points

• Which forecasting model to use depends on the grain.1
• This paper compares models at each grain on four public data sets (M5, six Dominick's categories, Breakfast at the Frat, and Corporación Favorita, the last kept out of all development), over rolling forecast origins with paired tests and model confidence sets.1
• The models are a world model that carries each item's state forward under the price and promotion plan, the same network without that state, LightGBM, the Temporal Fusion Transformer, TimesFM, Chronos-Bolt, Timestral, seasonal naive, and an equal-weight average of the world model, LightGBM and TimesFM given the plan.1
• For category-by-store planning cost, the average or LightGBM was lowest on seven of eight data sets, and the world model was lowest only on M5; it was behind LightGBM item by store on six of eight.1

Sources (1)

• [1]World Models, Trees and Foundation Models for Retail Forecasting: Evidence from Four Public Data Sets
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 18, 10:50 AM

  A retailer makes different decisions at different grains of the product, store and time hierarchy: store orders for an item in a store, supplier funding for an item across stores, category performance by store, and the financial plan for the chain.

  Which forecasting model to use depends on the grain.

Extractive summary: sentences quoted from the sources.