# Exploring the representativeness of the M5 competition data

arXiv: https://arxiv.org/abs/2103.02941 · HTML: https://mlsanity.com/paper/2103.02941/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 43 references that are in the map.
- First posted on arXiv 4 Mar 2021.

## Builds on (most cited first, 4 of 4)

- [catch22: CAnonical Time-series CHaracteristics](https://mlsanity.com/paper/1901.10200/) (2019)
- [Kaggle forecasting competitions: An overlooked learning opportunity](https://mlsanity.com/paper/2009.07701/) (2020)
- [GRATIS: GeneRAting TIme Series with diverse and controllable characteristics](https://mlsanity.com/paper/1903.02787/) (2019)
- [Forecasting with time series imaging](https://mlsanity.com/paper/1904.08064/) (2019)

## Built on by (most cited first, 2 of 2)

- [Foundation Models for Demand Forecasting via Dual-Strategy Ensembling](https://mlsanity.com/paper/2507.22053/) (2025)
- [Forecasting large collections of time series: feature-based methods](https://mlsanity.com/paper/2309.13807/) (2023)

## Abstract (arXiv)

The main objective of the M5 competition, which focused on forecasting the hierarchical unit sales of Walmart, was to evaluate the accuracy and uncertainty of forecasting methods in the field in order to identify best practices and highlight their practical implications. However, whether the findings of the M5 competition can be generalized and exploited by retail firms to better support their decisions and operation depends on the extent to which the M5 data is sufficiently similar to unit sales data of retailers that operate in different regions, sell different types of products, and consider different marketing strategies. To answer this question, we analyze the characteristics of the M5 time series and compare them with those of two grocery retailers, namely Corporaci'on Favorita and a major Greek supermarket chain, using feature spaces. Our results suggest that there are only small discrepancies between the examined data sets, supporting the representativeness of the M5 data.