rossmann-store-sales: papers linking to it

Kaggle · www.kaggle.com/competitions/rossmann-store-sales

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to rossmann-store-sales (https://www.kaggle.com/competitions/rossmann-store-sales), 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#

6 papers in the tracked journals link to the Kaggle dataset rossmann-store-sales in their Crossref records, the first in 2020; most of them (2) in 2025.
By category: AI (4), Operations Research (1) and Management (1); the journals with the most are Machine Learning (2), Computers & Industrial Engineering (1) and Journal of Business Research (1).

By year#

20202025

Papers linking to rossmann-store-sales by publication year
Year	Papers
2020	1
2021	1
2023	1
2024	1
2025	2

Journals#

Journals whose papers link to rossmann-store-sales most
Journal	Papers
Machine Learning	2
Computers & Industrial Engineering	1
Journal of Business Research	1
Pattern Recognition	1
Journal Of Big Data	1

Papers linking to rossmann-store-sales, most cited first
Published	Title	Journal	Authors	Citations	Link
2020-04-10	Survey on categorical data for neural networks	Journal Of Big Data	John Hancock, Taghi M. Khoshgoftaar	619	cites it
2021-07-08	Explainable boosted linear regression for time series forecasting	Pattern Recognition	Igor Ilic, Berk Görgülü, Mücahit Çevik et al.	84	cites it
2023-03-13	SETAR-Tree: a novel and accurate tree algorithm for global time series forecasting	Machine Learning	Rakshitha Godahewa, Geoffrey I. Webb, Daniel Francis Schmidt et al.	15	cites it
2024-07-03	Prediction-led prescription: Optimal Decision-Making in times of turbulence and business performance improvement	Journal of Business Research	Andreas Schäfers, Vasileios Bougioukos, Georgios Karamatzanis et al.	11	cites it
2025-02-24	TCR: topologically consistent reweighting for XGBoost in regression tasks	Machine Learning	Monty-Maximilian Zühlke, Daniel Kudenko⋆	7	cites it
2025-06-11	Supply chain demand forecasting based on multi-time scale data fusion network	Computers & Industrial Engineering	Yipeng Chen, Heng Zhang, Xingyou Yan et al.	6	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). rossmann-store-sales: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-competition-rossmann-store-sales

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

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