web-traffic-time-series-forecasting: papers linking to it

Kaggle · www.kaggle.com/competitions/web-traffic-time-series-forecasting

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to web-traffic-time-series-forecasting (https://www.kaggle.com/competitions/web-traffic-time-series-forecasting), 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 Kaggle dataset web-traffic-time-series-forecasting in their Crossref records, the first in 2021; most of them (2) in 2021.
By category: AI (3); the journals with the most are Expert Systems with Applications (1), Pattern Recognition (1) and Machine Learning (1).
2 of the papers build on one another: a later paper cites an earlier one that also links web-traffic-time-series-forecasting.

By year#

20212023

Papers linking to web-traffic-time-series-forecasting by publication year
Year	Papers
2021	2
2023	1

Journals#

Journals whose papers link to web-traffic-time-series-forecasting most
Journal	Papers
Expert Systems with Applications	1
Pattern Recognition	1
Machine Learning	1

Family tree#

Each paper sits under the earlier paper it builds on; papers that cite no other paper here start a branch of their own.

Global models for time series forecasting: A Simulation study Pattern Recognition, 2021
- SETAR-Tree: a novel and accurate tree algorithm for global time series forecasting Machine Learning, 2023 — cites the paper above

Papers linking to web-traffic-time-series-forecasting, most cited first
Published	Title	Journal	Authors	Citations	Link
2021-11-20	Global models for time series forecasting: A Simulation study	Pattern Recognition	Hansika Hewamalage, Christoph Bergmeir, Kasun Bandara	83	cites it
2021-11-08	A comparative study of nonlinear regression and autoregressive techniques in hybrid with particle swarm optimization for time-series forecasting	Expert Systems with Applications	Cry Kuranga, Nelishia Pillay	29	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

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). web-traffic-time-series-forecasting: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-competition-web-traffic-time-series-forecasting

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

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