Arturus/kaggle-web-traffic: papers linking to it

GitHub · 1,850 stars · 656 forks · Jupyter Notebook · license MIT · created 2017 · github.com/Arturus/kaggle-web-traffic

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to Arturus/kaggle-web-traffic (https://github.com/Arturus/kaggle-web-traffic), 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#

3 papers in the tracked journals link to the GitHub repository Arturus/kaggle-web-traffic in their Crossref records, the first in 2020; most of them (1) in 2023.
By category: AI (3); the journals with the most are Knowledge-Based Systems (1), Machine Learning (1) and Data Mining and Knowledge Discovery (1).

By year#

20202023

Papers linking to Arturus/kaggle-web-traffic by publication year
Year	Papers
2020	1
2022	1
2023	1

Journals#

Journals whose papers link to Arturus/kaggle-web-traffic most
Journal	Papers
Knowledge-Based Systems	1
Machine Learning	1
Data Mining and Knowledge Discovery	1

Papers linking to Arturus/kaggle-web-traffic, most cited first
Published	Title	Journal	Authors	Citations	Link
2022-12-02	Forecast evaluation for data scientists: common pitfalls and best practices	Data Mining and Knowledge Discovery	Hansika Hewamalage, Klaus Ackermann, Christoph Bergmeir	296	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
2020-04-23	Hybrid time-aligned and context attention for time series prediction	Knowledge-Based Systems	Zhumei Wang, Liang Zhang, Zhiming Ding	8	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). Arturus/kaggle-web-traffic: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-arturus-kaggle-web-traffic

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

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