m5-forecasting-accuracy: papers linking to it

Kaggle · www.kaggle.com/competitions/m5-forecasting-accuracy

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to m5-forecasting-accuracy (https://www.kaggle.com/competitions/m5-forecasting-accuracy), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

Key findings#

4 papers in the tracked journals link to the Kaggle dataset m5-forecasting-accuracy in their Crossref records, the first in 2021; most of them (1) in 2026.
By category: Operations Research (3) and AI (1); the journals with the most are European Journal of Operational Research (1), Supply Chain Analytics (1) and Machine Learning (1).

By year#

20212026

Papers linking to m5-forecasting-accuracy by publication year
Year	Papers
2021	1
2024	1
2025	1
2026	1

Journals#

Journals whose papers link to m5-forecasting-accuracy most
Journal	Papers
European Journal of Operational Research	1
Supply Chain Analytics	1
Machine Learning	1
Operations Research Forum	1

Papers linking to m5-forecasting-accuracy, most cited first
Published	Title	Journal	Authors	Citations	Link
2024-05-29	Exploiting residual errors in nonlinear online prediction	Machine Learning	Emirhan İlhan, Ahmet B. Koc, Süleyman S. Kozat	10	cites it
2021-07-17	Demand Forecasting of Individual Probability Density Functions with Machine Learning	Operations Research Forum	F. Wick, U. Kerzel, Martin Hahn et al.	8	cites it
2025-11-22	A hybrid learning framework for forecasting uncertainty and adaptive inventory planning in retail supply chains	Supply Chain Analytics	Ziyad Ahmad Mohammed, Anas Chafi, Mohammed El Hammoumi	7	cites it
2026-01-08	Ordering policies for multi-item inventory systems with correlated demands	European Journal of Operational Research	Zhaleh Rahimi, Douglas G. Down, Na Li et al.	0	cites it

How this is measured#

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

Cite this page

Scholar4AI. (2026). m5-forecasting-accuracy: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-competition-m5-forecasting-accuracy

@misc{scholar4ai-code-kaggle-competition-m5-forecasting-accuracy,
  author = {{Scholar4AI}},
  title = {m5-forecasting-accuracy: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/kaggle-competition-m5-forecasting-accuracy}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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